model : remove some ggml_concat (#27176)

Co-authored-by: Xuan Son Nguyen <son@huggingface.co>

common/arg.cpp

@@ -3646,6 +3646,18 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             }
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_REASONING"));
+    add_opt(common_arg(
+        {"--reasoning-effort"}, "LEVEL",
+        "reasoning effort level given to the chat template: 'default' to keep the template default,\n"
+        "or a level such as 'minimal', 'low', 'medium', 'high', 'xhigh' or 'max' (default: default)",
+        [](common_params & params, const std::string & value) {
+            if (value == "default") {
+                params.default_template_kwargs.erase("reasoning_effort");
+            } else {
+                params.default_template_kwargs["reasoning_effort"] = json(value).dump();
+            }
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_REASONING_EFFORT"));
     add_opt(common_arg(
         {"--reasoning-budget"}, "N",
         "token budget for thinking: -1 for unrestricted, 0 for immediate end, N>0 for token budget (default: -1)",
@@ -4065,6 +4077,9 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         {"--spec-draft-n-max"}, "N",
         string_format("number of tokens to draft for speculative decoding (default: %d)", params.speculative.draft.n_max),
         [](common_params & params, int value) {
+            if (value < 0) {
+                throw std::invalid_argument("invalid value");
+            }
             params.speculative.draft.n_max = value;
         }
     ).set_spec().set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_SPEC_DRAFT_N_MAX"));

common/chat.cpp

@@ -470,36 +470,80 @@ std::vector<common_chat_msg> common_chat_msgs_parse_oaicompat(const json & messa
     return msgs;
 }
 
+struct messages_inp_normalizer {
+    const jinja::caps & caps;
+
+    messages_inp_normalizer(const jinja::caps & c) : caps(c) {}
+
+    // handle supports_string_content / supports_typed_content
+    // if string=true and array=false, convert array to string
+    // if string=false and array=true, convert string to array
+    // if both are true, do nothing
+    json normalize(const json & messages) {
+        bool only_string = caps.supports_string_content && !caps.supports_typed_content;
+        bool only_typed  = !caps.supports_string_content && caps.supports_typed_content;
+        if ((!only_string && !only_typed) || !messages.is_array()) {
+            return messages;
+        }
+        json normalized = json::array();
+        for (const auto & msg : messages) {
+            json copy = msg;
+            auto it = copy.find("content");
+            if (it != copy.end()) {
+                if (only_typed && it->is_string()) {
+                    *it = json::array({
+                        json{
+                            {"type", "text"},
+                            {"text", it->get<std::string>()},
+                        }
+                    });
+                } else if (only_string && it->is_array()) {
+                    *it = concat_content_parts(*it);
+                }
+            }
+            normalized.push_back(std::move(copy));
+        }
+        return normalized;
+    }
+
+    // join parts with newline, do not add newline before or after media markers
+    static std::string concat_content_parts(const json & parts) {
+        std::string text;
+        bool last_was_media_marker = false;
+        for (const auto & part : parts) {
+            std::string type = part.value("type", "");
+            bool add_new_line = true;
+            if (type == "text") {
+                add_new_line = !last_was_media_marker && !text.empty();
+                last_was_media_marker = false;
+            } else if (type == "media_marker") {
+                add_new_line = false;
+                last_was_media_marker = true;
+            } else {
+                LOG_WRN("Ignoring content part type: %s\n", type.c_str());
+                continue;
+            }
+
+            if (add_new_line) {
+                text += '\n';
+            }
+
+            text += part.value("text", "");
+        }
+        return text;
+    }
+};
+
 static json render_message_to_json(const std::vector<common_chat_msg> & msgs, const jinja::caps & c) {
     if (!c.supports_string_content && !c.supports_typed_content) {
         LOG_WRN("%s: Neither string content nor typed content is supported by the template. This is unexpected and may lead to issues.\n", __func__);
     }
 
-    bool only_string_accepted =  c.supports_string_content && !c.supports_typed_content;
-    bool only_typed_accepted  = !c.supports_string_content &&  c.supports_typed_content;
-
     json messages = json::array();
     for (const auto & msg : msgs) {
-        if (only_string_accepted) {
-            json jmsg = msg.to_json_oaicompat(/* concat_typed_text= */ true);
-            messages.push_back(jmsg);
-        } else if (only_typed_accepted) {
-            json jmsg = msg.to_json_oaicompat(/* concat_typed_text= */ false);
-            if (jmsg.at("content").is_string()) {
-                jmsg["content"] = json::array({
-                    json{
-                        {"type", "text"},
-                        {"text", jmsg.at("content").get<std::string>()},
-                    }
-                });
-            }
-            messages.push_back(jmsg);
-        } else {
-            json jmsg = msg.to_json_oaicompat(/* concat_typed_text= */ false);
-            messages.push_back(jmsg);
-        }
+        messages.push_back(msg.to_json_oaicompat(/* concat_typed_text= */ false));
     }
-    return messages;
+    return messages_inp_normalizer(c).normalize(messages);
 }
 
 // DEPRECATED: only used in tests
@@ -892,8 +936,11 @@ static std::string common_chat_template_direct_apply_impl(
     const std::optional<json> & additional_context = std::nullopt) {
     jinja::context ctx(tmpl.source());
 
+    // messages_override is already built for this template, do not touch its content parts
     nlohmann::ordered_json inp = nlohmann::ordered_json{
-        {"messages", messages_override.has_value() ? *messages_override : inputs.messages},
+        {"messages", messages_override.has_value()
+            ? *messages_override
+            : messages_inp_normalizer(tmpl.original_caps()).normalize(inputs.messages)},
         {"bos_token", tmpl.bos_token()},
         {"eos_token", tmpl.eos_token()},
         {"enable_thinking", inputs.enable_thinking},
@@ -920,6 +967,10 @@ static std::string common_chat_template_direct_apply_impl(
         bool enabled = inp["preserve_reasoning"].get<bool>();
         jinja::caps_apply_preserve_reasoning(ctx, enabled);
     }
+    if (inp.contains("reasoning_effort") && inp["reasoning_effort"].is_string() && !inp["reasoning_effort"].empty()) {
+        std::string reasoning_effort = inp["reasoning_effort"].get<std::string>();
+        jinja::caps_apply_reasoning_effort(ctx, reasoning_effort);
+    }
 
     jinja::global_from_json(ctx, inp, inputs.mark_input);
 
@@ -953,14 +1004,12 @@ static std::string common_chat_template_generation_prompt_impl(
     const std::optional<json> & tools_override = std::nullopt,
     const std::optional<json> & additional_context = std::nullopt) {
 
-    auto adjusted_messages = messages_override ? *messages_override : inputs.messages;
-
     autoparser::generation_params params = inputs;
     params.add_generation_prompt = false;
     params.continue_final_message = COMMON_CHAT_CONTINUATION_NONE;
-    std::string no_gen_prompt    = common_chat_template_direct_apply_impl(tmpl, params, adjusted_messages, tools_override, additional_context);
+    std::string no_gen_prompt    = common_chat_template_direct_apply_impl(tmpl, params, messages_override, tools_override, additional_context);
     params.add_generation_prompt = true;
-    std::string gen_prompt       = common_chat_template_direct_apply_impl(tmpl, params, adjusted_messages, tools_override, additional_context);
+    std::string gen_prompt       = common_chat_template_direct_apply_impl(tmpl, params, messages_override, tools_override, additional_context);
 
     size_t prefix_len = 0;
     size_t min_size = std::min(no_gen_prompt.size(), gen_prompt.size());
@@ -2321,6 +2370,179 @@ static common_chat_params common_chat_params_init_deepseek_v3_2(const common_cha
     return data;
 }
 
+// Kimi K3 - XTML tagged format, built by open_tag/close_tag macros:
+//   open_tag(t, attrs) = <|open|>t k="v"...<|sep|>   close_tag(t) = <|close|>t<|sep|>
+//   assistant := [think] [response] [tools] close_tag(message) <|end_of_msg|>
+// the generation prompt already opens the think (or response) section, so the
+// section opener is optional here - same as Kimi K2 Thinking
+static common_chat_params common_chat_params_init_kimi_k3(const common_chat_template &          tmpl,
+                                                          const autoparser::generation_params & inputs) {
+    common_chat_params data;
+
+    data.prompt            = common_chat_template_direct_apply_impl(tmpl, inputs);
+    data.generation_prompt = common_chat_template_generation_prompt_impl(tmpl, inputs);
+    data.format            = COMMON_CHAT_FORMAT_PEG_NATIVE;
+    data.supports_thinking = true;
+
+    const std::string SEP         = "<|sep|>";
+    const std::string MSG_START   = "<|open|>message role=\"assistant\"<|sep|>";
+    const std::string THINK_START = "<|open|>think<|sep|>";
+    const std::string THINK_END   = "<|close|>think<|sep|>";
+    const std::string RESP_START  = "<|open|>response<|sep|>";
+    const std::string RESP_END    = "<|close|>response<|sep|>";
+    const std::string TOOLS_START = "<|open|>tools<|sep|>";
+    const std::string TOOLS_END   = "<|close|>tools<|sep|>";
+    const std::string CALL_START  = "<|open|>call tool=\"";
+    const std::string CALL_END    = "<|close|>call<|sep|>";
+    const std::string ARG_START   = "<|open|>argument key=\"";
+    const std::string ARG_END     = "<|close|>argument<|sep|>";
+    const std::string MSG_END     = "<|close|>message<|sep|>";
+    const std::string EOM_TOKEN   = "<|end_of_msg|>";
+
+    // only the markers are special tokens. tag names ("think", "response", ...) are
+    // normal tokens and must not be preserved, or prose with those words is broken
+    data.preserved_tokens = {
+        "<|open|>",
+        "<|close|>",
+        "<|sep|>",
+        "<|end_of_msg|>",
+    };
+
+    data.thinking_start_tag = THINK_START;
+    data.thinking_end_tags  = { THINK_END };
+
+    // per-role message-start delimiters. user/assistant messages only have the role
+    // attribute, so the full opener is used. system and tool messages have more
+    // attributes, so those delimiters stop after the closing quote of the role
+    data.message_delimiters = {
+        { COMMON_CHAT_ROLE_ASSISTANT, "<|open|>message role=\"assistant\"<|sep|>" },
+        { COMMON_CHAT_ROLE_USER,      "<|open|>message role=\"user\"<|sep|>"      },
+        { COMMON_CHAT_ROLE_TOOL,      "<|open|>message role=\"tool\""             },
+        { COMMON_CHAT_ROLE_SYSTEM,    "<|open|>message role=\"system\""           },
+    };
+
+    auto has_tools         = inputs.tools.is_array() && !inputs.tools.empty();
+    auto extract_reasoning = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE;
+    auto include_grammar   = has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE;
+
+    if (inputs.has_continuation()) {
+        const auto & msg = inputs.continue_msg;
+
+        data.generation_prompt = MSG_START + THINK_START + msg.reasoning_content;
+        if (inputs.continue_final_message == COMMON_CHAT_CONTINUATION_CONTENT) {
+            data.generation_prompt += THINK_END + RESP_START + msg.render_content();
+        }
+
+        data.prompt += data.generation_prompt;
+    }
+
+    auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) {
+        auto end = p.end();
+
+        auto start = p.optional(p.literal(MSG_START));
+
+        // the think section is always consumed, even with reasoning extraction off:
+        // the generation prompt ends with open_tag('think'), so it is always present.
+        // reasoning stops at its own closer, or at the response opener if the model
+        // skips the closer
+        auto think_body = extract_reasoning ? p.reasoning(p.until_one_of({ THINK_END, RESP_START })) :
+                                              p.content(p.until_one_of({ THINK_END, RESP_START }));
+
+        auto reasoning = p.optional(p.optional(p.literal(THINK_START)) + think_body +
+                                    p.optional(p.literal(THINK_END)));
+
+        // content runs to the response closer, or to the next section if truncated
+        auto response = p.optional(p.literal(RESP_START)) +
+                        p.content(p.until_one_of({ RESP_END, TOOLS_START, MSG_END })) +
+                        p.optional(p.literal(RESP_END));
+
+        // the EOG token after the message closer reaches the parser as text,
+        // so it must be consumed or the parse stays incomplete
+        auto trailer = p.optional(p.literal(MSG_END)) + p.optional(p.literal(EOM_TOKEN));
+
+        if (!has_tools || inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_NONE) {
+            return start + reasoning + response + trailer + end;
+        }
+
+        auto tool_choices = p.choice();
+        foreach_function(inputs.tools, [&](const json & tool) {
+            const auto & function = tool.at("function");
+            std::string  name     = function.at("name");
+            const json   schema   = function.contains("parameters") ? function.at("parameters") : json::object();
+
+            // arguments come one tag per key, with the JSON type in a type="..."
+            // attribute. the type is taken from the tool schema instead, as it tells
+            // us if the value is JSON or a literal string
+            auto args = p.eps();
+            if (schema.contains("properties") && !schema.at("properties").empty()) {
+                auto arg_choices = p.choice();
+                for (const auto & prop : schema.at("properties").items()) {
+                    const std::string & key = prop.key();
+
+                    std::string type = "string";
+                    if (prop.value().is_object() && prop.value().contains("type") &&
+                        prop.value().at("type").is_string()) {
+                        type = prop.value().at("type").get<std::string>();
+                    }
+
+                    auto value = type == "string" ? p.tool_arg_string_value(p.until(ARG_END)) :
+                                                    p.tool_arg_value(p.until(ARG_END));
+
+                    // skip the trailing type="..." attribute: anything up to <|sep|>
+                    arg_choices |= p.rule("kimi-k3-arg-" + name + "-" + key,
+                                          p.tool_arg(p.tool_arg_open(p.literal(ARG_START)) +
+                                                     p.tool_arg_name(p.literal(key)) + p.literal("\"") +
+                                                     p.until(SEP) + p.literal(SEP) + value +
+                                                     p.tool_arg_close(p.literal(ARG_END))));
+                }
+                args = p.zero_or_more(arg_choices);
+            }
+
+            // skip the trailing index="N" attribute the same way
+            auto call = p.tool(p.tool_open(p.literal(CALL_START) + p.tool_name(p.literal(name)) + p.literal("\"") +
+                                           p.until(SEP) + p.literal(SEP)) +
+                               p.tool_args(args) + p.tool_close(p.literal(CALL_END)));
+
+            tool_choices |= p.rule("kimi-k3-tool-" + name, call);
+        });
+
+        // all calls go inside one tools section, then the message is closed. the
+        // message closer is part of the trigger rule, or else the lazy grammar
+        // rejects it once tool calls have started
+        auto tools_section =
+            p.trigger_rule("kimi-k3-tool-call", p.literal(TOOLS_START) + p.one_or_more(tool_choices) +
+                                                    p.literal(TOOLS_END) + p.optional(p.literal(MSG_END)) +
+                                                    p.optional(p.literal(EOM_TOKEN)));
+
+        auto tools = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED ? tools_section :
+                                                                              p.optional(tools_section);
+
+        return start + reasoning + response + tools + trailer + end;
+    });
+
+    data.parser = parser.save();
+
+    if (include_grammar) {
+        data.grammar_lazy = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED;
+        data.grammar      = build_grammar([&](const common_grammar_builder & builder) {
+            foreach_function(inputs.tools, [&](const json & tool) {
+                const auto & function = tool.at("function");
+                if (function.contains("parameters")) {
+                    auto schema = function.at("parameters");
+                    builder.resolve_refs(schema);
+                }
+            });
+            parser.build_grammar(builder, data.grammar_lazy);
+        });
+
+        data.grammar_triggers = {
+            { COMMON_GRAMMAR_TRIGGER_TYPE_WORD, TOOLS_START },
+        };
+    }
+
+    return data;
+}
+
 // Cohere2 MoE (a.k.a. "North Code") parser.
 //
 // The assistant turn is fully marker-wrapped:
@@ -3289,6 +3511,13 @@ std::optional<common_chat_params> common_chat_try_specialized_template(
         return common_chat_params_init_kimi_k2(tmpl, params);
     }
 
+    // Kimi K3 - the <|open|>/<|close|>/<|end_of_msg|> markers are unique to it
+    if (src.find("<|open|>") != std::string::npos && src.find("<|close|>") != std::string::npos &&
+        src.find("<|end_of_msg|>") != std::string::npos) {
+        LOG_DBG("Using specialized template: Kimi K3\n");
+        return common_chat_params_init_kimi_k3(tmpl, params);
+    }
+
     // Cohere2 MoE / North Code - marker-wrapped format with <|START_TEXT|> content and
     // <|START_ACTION|> JSON tool calls. <|START_TEXT|> is unique to this template (the older
     // Command-R templates use <|START_RESPONSE|>).

common/imatrix-loader.cpp

@@ -102,7 +102,8 @@ bool common_imatrix_load(const std::string & fname, common_imatrix & imatrix) {
     const int64_t chunk_count_key = gguf_find_key(ctx_gguf, LLM_KV_IMATRIX_CHUNK_COUNT);
     const int64_t chunk_size_key  = gguf_find_key(ctx_gguf, LLM_KV_IMATRIX_CHUNK_SIZE);
 
-    if (datasets_key != -1 && gguf_get_arr_type(ctx_gguf, datasets_key) == GGUF_TYPE_STRING) {
+    if (datasets_key != -1 && gguf_get_kv_type(ctx_gguf, datasets_key) == GGUF_TYPE_ARRAY &&
+        gguf_get_arr_type(ctx_gguf, datasets_key) == GGUF_TYPE_STRING) {
         const int64_t n = gguf_get_arr_n(ctx_gguf, datasets_key);
         imatrix.datasets.reserve(imatrix.datasets.size() + n);
         for (int64_t i = 0; i < n; ++i) {
@@ -143,6 +144,13 @@ bool common_imatrix_load(const std::string & fname, common_imatrix & imatrix) {
             return false;
         }
 
+        if (in_sum2->type != GGML_TYPE_F32 || counts->type != GGML_TYPE_F32) {
+            LOG_ERR("%s: sums and counts for %s must be F32\n", __func__, name.c_str());
+            gguf_free(ctx_gguf);
+            ggml_free(ctx);
+            return false;
+        }
+
         auto & e = imatrix.entries[name];
 
         const int64_t nval    = ggml_nelements(in_sum2);

common/jinja/caps.cpp

@@ -17,13 +17,19 @@ namespace jinja {
 
 using caps_json_fn = std::function<json()>;
 using caps_ctx_fn = std::function<void(context &)>;
-using caps_analyze_fn = std::function<void(bool, value &, value &, const std::string &)>;
+using caps_analyze_fn = std::function<void(context &, bool, value &, value &, const std::string &)>;
 
 void caps_apply_preserve_reasoning(jinja::context & ctx, bool enabled) {
     ctx.set_val("preserve_thinking",         mk_val<value_bool>(enabled));
     ctx.set_val("clear_thinking",            mk_val<value_bool>(!enabled));
     ctx.set_val("truncate_history_thinking", mk_val<value_bool>(!enabled));
-    ctx.set_val("drop_thinking",            mk_val<value_bool>(!enabled));
+    ctx.set_val("drop_thinking",             mk_val<value_bool>(!enabled));
+}
+
+void caps_apply_reasoning_effort(jinja::context & ctx, const std::string & effort) {
+    value var = mk_val<value_string>(effort); // bind to the same value for stats
+    ctx.set_val("reasoning_effort",   var);
+    ctx.set_val("reasoning_strength", var);
 }
 
 static void caps_try_execute(jinja::program & prog,
@@ -62,7 +68,7 @@ static void caps_try_execute(jinja::program & prog,
         // ignore exceptions during capability analysis
     }
 
-    analyze_fn(success, messages, tools, result);
+    analyze_fn(ctx, success, messages, tools, result);
 }
 
 // for debugging only
@@ -87,6 +93,7 @@ std::map<std::string, bool> caps::to_map() const {
         {"supports_parallel_tool_calls", supports_parallel_tool_calls},
         {"supports_system_role", supports_system_role},
         {"supports_preserve_reasoning", supports_preserve_reasoning},
+        {"supports_reasoning_effort", supports_reasoning_effort},
         {"supports_object_arguments", supports_object_arguments},
     };
 }
@@ -110,6 +117,8 @@ caps caps_get(jinja::program & prog) {
 
     JJ_DEBUG("%s\n", ">>> Running capability check: typed content");
 
+    static const std::string content_marker = "STRING_MARKER";
+
     // case: typed content support
     caps_try_execute(
         prog,
@@ -118,22 +127,26 @@ caps caps_get(jinja::program & prog) {
             return json::array({
                 {
                     {"role", "user"},
-                    {"content", "content"}
+                    {"content", content_marker}
                 }
             });
         },
         nullptr, // ctx_fn
         nullptr, // tools_fn
-        [&](bool success, value & messages, value &, const std::string &) {
+        [&](context &, bool success, value & messages, value &, const std::string & rendered) {
             auto & content = messages->at(0)->at("content");
             caps_print_stats(content, "messages[0].content");
-            if (has_op(content, "selectattr") || has_op(content, "array_access")) {
+            bool used_as_array = has_op(content, "selectattr") || has_op(content, "array_access");
+            if (used_as_array) {
                 // accessed as an array
                 result.supports_typed_content = true;
             }
             if (!success) {
                 // failed to execute with content as string
                 result.supports_string_content = false;
+            } else if (used_as_array && rendered.find(content_marker) == std::string::npos) {
+                // edge case: string may be accessed for checking, but does not appear in the output
+                result.supports_string_content = false;
             }
         }
     );
@@ -158,7 +171,7 @@ caps caps_get(jinja::program & prog) {
         },
         nullptr, // ctx_fn
         nullptr, // tools_fn
-        [&](bool, value & messages, value &, const std::string &) {
+        [&](context &, bool, value & messages, value &, const std::string &) {
             auto & content = messages->at(0)->at("content");
             caps_print_stats(content, "messages[0].content");
             if (!content->stats.used) {
@@ -234,7 +247,7 @@ caps caps_get(jinja::program & prog) {
                 },
             });
         },
-        [&](bool success, value & messages, value & tools, const std::string &) {
+        [&](context &, bool success, value & messages, value & tools, const std::string &) {
             if (!success) {
                 return; // Nothing can be inferred
             }
@@ -327,7 +340,7 @@ caps caps_get(jinja::program & prog) {
                     },
                 });
             },
-            [&](bool success, value & messages, value & tools, const std::string &) {
+            [&](context &, bool success, value & messages, value & tools, const std::string &) {
                 if (!success) {
                     result.supports_tool_calls = false;
                     result.supports_tools = false;
@@ -429,7 +442,7 @@ caps caps_get(jinja::program & prog) {
                 },
             });
         },
-        [&](bool success, value & messages, value &, const std::string &) {
+        [&](context &, bool success, value & messages, value &, const std::string &) {
             if (!success) {
                 result.supports_parallel_tool_calls = false;
                 return;
@@ -486,7 +499,7 @@ caps caps_get(jinja::program & prog) {
             caps_apply_preserve_reasoning(ctx, true);
         },
         nullptr, // tools_fn
-        [&](bool, value &, value &, const std::string & output) {
+        [&](context &, bool, value &, value &, const std::string & output) {
             // note: we cannot use stats here because the reasoning_content may be used for "if" condition test, but not actually outputted in the final result
             if (output.find(reasoning_placeholder) != std::string::npos) {
                 result.supports_preserve_reasoning = true;
@@ -494,6 +507,32 @@ caps caps_get(jinja::program & prog) {
         }
     );
 
+    JJ_DEBUG("%s\n", ">>> Running capability check: reasoning effort");
+
+    // case: reasoning effort level
+    caps_try_execute(
+        prog,
+        [&]() {
+            // messages
+            return json::array({
+                {
+                    {"role", "user"},
+                    {"content", "User message"}
+                },
+            });
+        },
+        [&](context & ctx) {
+            ctx.set_val("enable_thinking", mk_val<value_bool>(true));
+            caps_apply_reasoning_effort(ctx, "low");
+        },
+        nullptr, // tools_fn
+        [&](context & ctx, bool, value &, value &, const std::string &) {
+            value effort = ctx.get_val("reasoning_effort");
+            caps_print_stats(effort, "reasoning_effort");
+            result.supports_reasoning_effort = effort->stats.used;
+        }
+    );
+
     JJ_DEBUG("%s\n", result.to_string().c_str());
 
     return result;

common/jinja/caps.h

@@ -16,6 +16,9 @@ struct caps {
     // supports preserve reasoning trace in the full history, not just the last assistant message
     bool supports_preserve_reasoning = false;
 
+    // supports reasoning effort levels
+    bool supports_reasoning_effort = false;
+
     // one of the 2 content capabilities must be true
     bool supports_string_content = true;
     bool supports_typed_content = false;
@@ -32,5 +35,6 @@ struct caps {
 caps caps_get(jinja::program & prog);
 
 void caps_apply_preserve_reasoning(jinja::context & ctx, bool enabled);
+void caps_apply_reasoning_effort(jinja::context & ctx, const std::string & effort);
 
 } // namespace jinja

common/jinja/runtime.cpp

@@ -263,7 +263,7 @@ value binary_expression::execute_impl(context & ctx) {
             return res;
         }
         for (int64_t i = 0; i < repeat; ++i) {
-            res->val_str = res->val_str.append(str);
+            res->val_str.append(str);
         }
         return res;
     }

common/jinja/runtime.h

@@ -763,14 +763,22 @@ struct runtime {
         gather_string_parts_recursive(val, parts);
         // join consecutive parts with the same type
         auto & p = parts->val_str.parts;
-        for (size_t i = 1; i < p.size(); ) {
-            if (p[i].is_input == p[i - 1].is_input) {
-                p[i - 1].val += p[i].val;
-                p.erase(p.begin() + i);
+        if (p.empty()) {
+            return parts;
+        }
+        size_t w = 0;
+        for (size_t r = 1; r < p.size(); r++) {
+            if (p[w].is_input == p[r].is_input) {
+                p[w].val += p[r].val;
             } else {
-                i++;
+                w++;
+                if (w != r) {
+                    // the guard is needed, self-move leaves the string in an unspecified state
+                    p[w] = std::move(p[r]);
+                }
             }
         }
+        p.resize(w + 1);
         return parts;
     }
 

common/jinja/string.cpp

@@ -103,7 +103,7 @@ void string::mark_input_based_on(const string & other) {
     }
 }
 
-string string::append(const string & other) {
+string & string::append(const string & other) {
     for (const auto & part : other.parts) {
         parts.push_back(part);
     }

common/jinja/string.h

@@ -47,7 +47,7 @@ struct string {
     // mark this string as input if other has ALL parts as input
     void mark_input_based_on(const string & other);
 
-    string append(const string & other);
+    string & append(const string & other);
 
     // in-place transformations
 

common/preset.cpp

@@ -365,8 +365,25 @@ struct local_model {
     std::string name;
     std::string path;
     std::string path_mmproj;
+    std::string path_draft;
 };
 
+// TODO @ngxson: handle "eagle3-" when it's supported by common_speculative_types_from_gguf()
+static const char * draft_prefixes[] = { "mtp-", "dspark-", "dflash-" };
+
+static bool is_mmproj_file(const std::string & fname) {
+    return fname.find("mmproj") != std::string::npos;
+}
+
+static bool is_draft_file(const std::string & fname) {
+    for (const auto & prefix : draft_prefixes) {
+        if (fname.rfind(prefix, 0) == 0) {
+            return true;
+        }
+    }
+    return false;
+}
+
 common_presets common_preset_context::load_from_models_dir(const std::string & models_dir) const {
     if (!std::filesystem::exists(models_dir) || !std::filesystem::is_directory(models_dir)) {
         throw std::runtime_error(string_format("error: '%s' does not exist or is not a directory\n", models_dir.c_str()));
@@ -378,10 +395,15 @@ common_presets common_preset_context::load_from_models_dir(const std::string & m
         common_file_info model_file;
         common_file_info first_shard_file;
         common_file_info mmproj_file;
+        common_file_info draft_file;
         for (const auto & file : files) {
             if (string_ends_with(file.name, ".gguf")) {
-                if (file.name.find("mmproj") != std::string::npos) {
+                if (is_mmproj_file(file.name)) {
                     mmproj_file = file;
+                } else if (is_draft_file(file.name)) {
+                    if (draft_file.path.empty()) {
+                        draft_file = file; // first sidecar found wins
+                    }
                 } else if (file.name.find("-00001-of-") != std::string::npos) {
                     first_shard_file = file;
                 } else {
@@ -393,7 +415,8 @@ common_presets common_preset_context::load_from_models_dir(const std::string & m
         local_model model{
             /* name        */ name,
             /* path        */ first_shard_file.path.empty() ? model_file.path : first_shard_file.path,
-            /* path_mmproj */ mmproj_file.path // can be empty
+            /* path_mmproj */ mmproj_file.path, // can be empty
+            /* path_draft  */ draft_file.path   // can be empty
         };
         if (!model.path.empty()) {
             models.push_back(model);
@@ -405,13 +428,17 @@ common_presets common_preset_context::load_from_models_dir(const std::string & m
         if (file.is_dir) {
             scan_subdir(file.path, file.name);
         } else if (string_ends_with(file.name, ".gguf")) {
+            if (is_mmproj_file(file.name) || is_draft_file(file.name)) {
+                continue; // companion file, cannot be loaded as a model on its own
+            }
             // single file model
             std::string name = file.name;
             string_replace_all(name, ".gguf", "");
             local_model model{
                 /* name        */ name,
                 /* path        */ file.path,
-                /* path_mmproj */ ""
+                /* path_mmproj */ "",
+                /* path_draft  */ ""
             };
             models.push_back(model);
         }
@@ -426,6 +453,9 @@ common_presets common_preset_context::load_from_models_dir(const std::string & m
         if (!model.path_mmproj.empty()) {
             preset.set_option(*this, "LLAMA_ARG_MMPROJ", model.path_mmproj);
         }
+        if (!model.path_draft.empty()) {
+            preset.set_option(*this, "LLAMA_ARG_SPEC_DRAFT_MODEL", model.path_draft);
+        }
         out[preset.name] = preset;
     }
 

conversion/__init__.py

@@ -125,6 +125,7 @@ TEXT_MODEL_MAP: dict[str, str] = {
     "JinaEmbeddingsV5Model": "bert",
     "KORMoForCausalLM": "qwen",
     "KimiK25ForConditionalGeneration": "deepseek",
+    "KimiK3ForConditionalGeneration": "kimi_k3",
     "KimiLinearForCausalLM": "kimi_linear",
     "KimiLinearModel": "kimi_linear",
     "KimiVLForConditionalGeneration": "deepseek",
@@ -161,6 +162,8 @@ TEXT_MODEL_MAP: dict[str, str] = {
     "MiniCPM3ForCausalLM": "minicpm",
     "MiniCPMForCausalLM": "minicpm",
     "MiniCPMV4_6ForConditionalGeneration": "minicpm",
+    "MiniMaxText01ForCausalLM": "minimax",
+    "MiniMaxM1ForCausalLM": "minimax",
     "MiniMaxM2ForCausalLM": "minimax",
     "MiniMaxM3SparseForCausalLM": "minimax",
     "MiniMaxM3SparseForConditionalGeneration": "minimax",

conversion/base.py

@@ -658,6 +658,43 @@ class ModelBase:
     def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
         return ()
 
+    @staticmethod
+    def repack_mxfp4_blocks(packed: Tensor, scale: Tensor) -> np.ndarray:
+        """
+        Repack 4-bit MX weights into ggml `block_mxfp4`. Lossless - only moves bits.
+
+        Source (compressed-tensors "mxfp4-pack-quantized", also used by DeepSeek-V4):
+          packed  uint8 [rows, cols/2]  element 2i in the low nibble, 2i+1 in the high one
+          scale   uint8 [rows, cols/32] one E8M0 biased exponent per 32-element group
+
+        Destination, per group: one scale byte then 16 code bytes, where byte j holds
+        element j in the low nibble and element j+16 in the high one.
+
+        The 4-bit codes need no remapping: both sides index into ggml's kvalues_mxfp4
+        order. ggml doubles the kvalues and halves the scale, so the value is the same.
+        """
+        p = packed.contiguous().view(torch.uint8)
+        s = scale.contiguous().view(torch.uint8)
+
+        rows, packed_cols = p.shape
+        cols = packed_cols * 2
+        if cols % 32 != 0:
+            raise ValueError(f"MXFP4 source row has {cols} values, expected a multiple of 32")
+
+        n_blocks = cols // 32
+        if tuple(s.shape) != (rows, n_blocks):
+            raise ValueError(f"MXFP4 scale shape {tuple(s.shape)} does not match {(rows, n_blocks)}")
+
+        src = p.reshape(rows, n_blocks, 16)
+        lo = src & 0x0F           # elements 0, 2, 4, ...
+        hi = (src >> 4) & 0x0F    # elements 1, 3, 5, ...
+
+        vals = torch.stack((lo, hi), dim=-1).reshape(rows, n_blocks, 32)
+        qs = vals[:, :, :16] | (vals[:, :, 16:] << 4)
+
+        raw = torch.cat((s.unsqueeze(-1), qs.to(torch.uint8)), dim=-1)
+        return raw.reshape(rows, n_blocks * 17).cpu().numpy()
+
     @staticmethod
     def _nvfp4_pack(weight: Tensor, scale: Tensor) -> tuple[np.ndarray, list[int]]:
         """Repack NVFP4 ModelOpt tensors into ggml super-block layout.
@@ -2661,7 +2698,10 @@ def get_model_architecture(hparams: dict[str, Any], model_type: ModelType) -> st
     # Step3-VL keeps text config under text_config but uses a custom top-level architecture.
     # For text conversion we route to a dedicated text-only class.
     # TODO: refactor this later to avoid adding exception here
-    if model_type == ModelType.TEXT and arch in ("StepVLForConditionalGeneration", "Sarashina2VisionForCausalLM", "Exaone4_5_ForConditionalGeneration", "Step3p7ForConditionalGeneration"):
+    # Kimi-K3's text_config reports "KimiLinearForCausalLM", which is the older
+    # Kimi-Linear-48B architecture and cannot load K3 (no attention residuals,
+    # latent MoE, situ, ...). Route on the top-level architecture instead.
+    if model_type == ModelType.TEXT and arch in ("StepVLForConditionalGeneration", "Sarashina2VisionForCausalLM", "Exaone4_5_ForConditionalGeneration", "Step3p7ForConditionalGeneration", "KimiK3ForConditionalGeneration"):
         return arch
 
     # if "architectures" is found in the sub-config, use that instead

conversion/deepseek.py

@@ -709,31 +709,6 @@ class DeepseekV4Model(TextModel):
         for name in tensors_to_remove:
             del self.model_tensors[name]
 
-    @staticmethod
-    def _pack_mxfp4_blocks(weight: Tensor, scale: Tensor) -> np.ndarray:
-        packed = weight.contiguous().view(torch.uint8)
-        scale_u8 = scale.contiguous().view(torch.uint8)
-
-        out_features, packed_cols = packed.shape
-        logical_cols = packed_cols * 2
-        if logical_cols % 32 != 0:
-            raise ValueError(f"MXFP4 source row has {logical_cols} values, expected a multiple of 32")
-
-        n_blocks = logical_cols // 32
-        if tuple(scale_u8.shape) != (out_features, n_blocks):
-            raise ValueError(f"MXFP4 scale shape {tuple(scale_u8.shape)} does not match {(out_features, n_blocks)}")
-
-        src = packed.reshape(out_features, n_blocks, 16)
-        low = src & 0x0F
-        high = (src >> 4) & 0x0F
-
-        # The safetensors bytes store adjacent values as low/high nibbles.
-        # ggml MXFP4 blocks store values 0..15 in low nibbles and 16..31 in high nibbles.
-        vals = torch.stack((low, high), dim=-1).reshape(out_features, n_blocks, 32)
-        qs = vals[:, :, :16] | (vals[:, :, 16:] << 4)
-        raw = torch.cat((scale_u8.unsqueeze(-1), qs.to(torch.uint8)), dim=-1)
-        return raw.reshape(out_features, n_blocks * 17).cpu().numpy()
-
     def _write_mxfp4_expert_tensor(self, bid: int, proj: str, tensor_key: gguf.MODEL_TENSOR) -> list[str]:
         n_experts = self.hparams["n_routed_experts"]
         data: np.ndarray | None = None
@@ -747,7 +722,7 @@ class DeepseekV4Model(TextModel):
 
             weight = LazyTorchTensor.to_eager(self.model_tensors[weight_name]())
             scale = LazyTorchTensor.to_eager(self.model_tensors[scale_name]())
-            packed = self._pack_mxfp4_blocks(weight, scale)
+            packed = self.repack_mxfp4_blocks(weight, scale)
             if data is None:
                 data = np.empty((n_experts, *packed.shape), dtype=packed.dtype)
             data[eid] = packed

conversion/kimi_k3.py

@@ -0,0 +1,375 @@
+from __future__ import annotations
+
+import re
+from pathlib import Path
+from typing import Callable, Iterable, Iterator, TYPE_CHECKING
+
+import numpy as np
+import torch
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import LazyTorchTensor, ModelBase, TextModel, gguf, logger
+
+from .kimi_linear import KimiLinearModel
+
+
+@ModelBase.register("KimiK3ForConditionalGeneration")
+class KimiK3Model(TextModel):
+    """
+    Kimi-K3 text model (KimiLinearForCausalLM under a `language_model.` prefix).
+
+    Shares the hybrid MLA + KDA skeleton with kimi-linear, but that converter
+    cannot load it: K3 adds cross-layer attention residuals, a latent MoE, the
+    situ activation, an MLA output gate and a full-rank KDA gate.
+
+    The vision tower and mm_projector are skipped - text only for now.
+    """
+
+    model_arch = gguf.MODEL_ARCH.KIMI_K3
+
+    _experts: list[dict[str, Tensor]] | None = None
+
+    # `<x>_res_norm.weight` and `<x>_res_proj.weight` are only used as their
+    # elementwise product, so they are fused into one [n_embd] vector here.
+    # they arrive apart, so buffer the first one and tag it with its kind.
+    _res_parts: dict[str, tuple[str, Tensor]]
+
+    # HF suffix -> (gguf tensor, per-layer?)
+    _RES_FUSIONS = {
+        "self_attention_res": (gguf.MODEL_TENSOR.ATTN_RES_SCORE, True),
+        "mlp_res":            (gguf.MODEL_TENSOR.FFN_RES_SCORE,   True),
+        "output_attn_res":    (gguf.MODEL_TENSOR.OUTPUT_RES_SCORE, False),
+    }
+
+    # compressed-tensors MXFP4. the `language_model.` prefix is still there, as
+    # self.model_tensors is keyed by the raw checkpoint names
+    _MXFP4_FORMAT = "mxfp4-pack-quantized"
+    _MXFP4_EXPERT_RE = re.compile(
+        r"^(?:language_model\.)?model\.layers\.(\d+)"
+        r"\.block_sparse_moe\.experts\.(\d+)\.(w[123])\.weight_packed$"
+    )
+    _MXFP4_PROJ = {
+        "w1": gguf.MODEL_TENSOR.FFN_GATE_EXP,
+        "w2": gguf.MODEL_TENSOR.FFN_DOWN_EXP,
+        "w3": gguf.MODEL_TENSOR.FFN_UP_EXP,
+    }
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self._res_parts = {}
+
+    def set_vocab(self):
+        # K3 has the same TikToken vocab as K2, so kimi-linear's vocab handling works.
+        # borrowed, not inherited: the method only touches TextModel members, and K3
+        # shares none of kimi-linear's tensor layout.
+        KimiLinearModel.set_vocab(self)  # ty: ignore[invalid-argument-type]
+
+        # ...but that forces eos to the tokenizer's eos_id, which is [EOS], the
+        # document terminator. K3's config says <|end_of_msg|>, the turn terminator;
+        # with [EOS] the generation never stops at the end of a turn.
+        if (eos := self.hparams.get("eos_token_id")) is not None:
+            logger.info(f"restoring configured eos_token_id {eos} (kimi-linear forces the tokenizer's)")
+            self.gguf_writer.add_eos_token_id(eos)
+
+        # K3 renders chats in python (encoding_k3.py) and ships no jinja template,
+        # so add the bundled one when the model has none
+        if gguf.SpecialVocab(self.dir_model, load_merges=False).chat_template is None:
+            template_path = Path(__file__).parent.parent / "models" / "templates" / "Kimi-K3.jinja"
+            logger.info(f"gguf: model has no chat template, using {template_path.name}")
+            self.gguf_writer.add_chat_template(template_path.read_text(encoding="utf-8"))
+
+    #
+    # compressed-tensors MXFP4 -> ggml MXFP4
+    #
+
+    def _is_mxfp4_packed(self) -> bool:
+        quant_config = self.hparams.get("quantization_config") or {}
+        return (quant_config.get("quant_method") == "compressed-tensors"
+                and quant_config.get("format") == self._MXFP4_FORMAT)
+
+    def dequant_model(self):
+        if not self._is_mxfp4_packed():
+            return super().dequant_model()
+
+        # skipping base.py's dequant is only safe if the experts are the only
+        # quantized tensors, so check it
+        stray = [n for n in self.model_tensors
+                 if n.endswith(".weight_packed") and not self._MXFP4_EXPERT_RE.match(n)]
+        if stray:
+            raise NotImplementedError(
+                f"{len(stray)} MXFP4 tensor(s) outside the routed experts, e.g. {stray[0]!r}; "
+                "only the routed experts have a repack path"
+            )
+
+    def _mxfp4_expert_tensor(self, loaders: list[tuple[Callable[[], Tensor], Callable[[], Tensor]]]):
+        """
+        One stacked [n_expert, rows, cols] MXFP4 tensor, built lazily.
+
+        gguf_writer holds every added tensor until the final write, so building
+        this eagerly (like the DeepSeek-V4 path does) keeps all ~1.38 TB of
+        experts in memory. lazy means only the tensor being written is resident.
+        """
+        # meta shapes, so this does not read any weights
+        rows, packed_cols = loaders[0][0]().shape
+        n_blocks = (packed_cols * 2) // 32
+        byte_shape = (len(loaders), rows, n_blocks * 17)
+
+        def load(fns: list[tuple[Callable[[], Tensor], Callable[[], Tensor]]]) -> np.ndarray:
+            out = np.empty(byte_shape, dtype=np.uint8)
+            for eid, (packed_fn, scale_fn) in enumerate(fns):
+                out[eid] = self.repack_mxfp4_blocks(
+                    LazyTorchTensor.to_eager(packed_fn()),
+                    LazyTorchTensor.to_eager(scale_fn()),
+                )
+            return out
+
+        # loaders goes through args, not the closure, so that `func` matches
+        # LazyBase's single-argument shape
+        return gguf.LazyNumpyTensor(
+            meta=gguf.LazyNumpyTensor.meta_with_dtype_and_shape(np.uint8, byte_shape),
+            args=(loaders,),
+            func=load,
+        )
+
+    def _write_mxfp4_experts(self) -> None:
+        n_experts = self.hparams["num_experts"]
+
+        # (bid, wid) -> {expert id: (packed name, scale name)}
+        groups: dict[tuple[int, str], dict[int, tuple[str, str]]] = {}
+        for name in self.model_tensors:
+            m = self._MXFP4_EXPERT_RE.match(name)
+            if m is None:
+                continue
+            bid, eid, wid = int(m.group(1)), int(m.group(2)), m.group(3)
+            scale_name = name.removesuffix("_packed") + "_scale"
+            if scale_name not in self.model_tensors:
+                raise KeyError(f"missing {scale_name} for {name}")
+            groups.setdefault((bid, wid), {})[eid] = (name, scale_name)
+
+        consumed: list[str] = []
+        for (bid, wid), experts in sorted(groups.items()):
+            missing = [e for e in range(n_experts) if e not in experts]
+            if missing:
+                raise KeyError(
+                    f"layer {bid} {wid}: {len(missing)} of {n_experts} experts missing, "
+                    f"first is {missing[0]}"
+                )
+            if len(experts) != n_experts:
+                raise KeyError(f"layer {bid} {wid}: {len(experts)} experts, expected {n_experts}")
+
+            loaders = []
+            for eid in range(n_experts):
+                packed_name, scale_name = experts[eid]
+                loaders.append((self.model_tensors[packed_name], self.model_tensors[scale_name]))
+                consumed += [packed_name, scale_name]
+
+            data = self._mxfp4_expert_tensor(loaders)
+            new_name = self.format_tensor_name(self._MXFP4_PROJ[wid], bid)
+            shape = gguf.quant_shape_from_byte_shape(data.shape, gguf.GGMLQuantizationType.MXFP4)
+            logger.info(
+                f"{new_name}: repacked {n_experts} experts to MXFP4, "
+                f"shape = {{{', '.join(str(n) for n in reversed(shape))}}}"
+            )
+            self.gguf_writer.add_tensor(new_name, data, raw_dtype=gguf.GGMLQuantizationType.MXFP4)
+
+        for name in consumed:
+            del self.model_tensors[name]
+
+    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
+        # not a generator on purpose: base.py chains this with get_tensors(), so the
+        # tensors used here must be removed from model_tensors before that starts
+        if self._is_mxfp4_packed():
+            self._write_mxfp4_experts()
+        return ()
+
+    def get_tensors(self) -> Iterator[tuple[str, Tensor]]:
+        for name, data in super().get_tensors():
+            if name.startswith(("vision_tower.", "mm_projector.")):
+                continue  # text only
+            if name.startswith("language_model."):
+                name = name[len("language_model."):]
+            yield name, data
+
+    def set_gguf_parameters(self):
+        # MLA is served as MQA with a single large head, then decompressed
+        self.hparams["num_key_value_heads"] = 1
+
+        super().set_gguf_parameters()
+        self.gguf_writer.add_vocab_size(self.hparams["vocab_size"])
+
+        linear_attn_config = self.hparams["linear_attn_config"]
+
+        # n_head_kv == 0 marks a KDA (recurrent) layer. the layer lists are 1-indexed,
+        # as KimiLinearConfig.is_kda_layer uses (layer_idx + 1)
+        full_attn_layers = linear_attn_config["full_attn_layers"]
+        n_kv_heads = [
+            self.hparams["num_key_value_heads"] if (il + 1) in full_attn_layers else 0
+            for il in range(self.hparams["num_hidden_layers"])
+        ]
+        assert len(n_kv_heads) == self.hparams["num_hidden_layers"]
+        self.gguf_writer.add_head_count_kv(n_kv_heads)
+
+        # --- KDA ---
+        self.gguf_writer.add_ssm_conv_kernel(linear_attn_config["short_conv_kernel_size"])
+        self.gguf_writer.add_kda_head_dim(linear_attn_config["head_dim"])
+        if (lb := linear_attn_config.get("gate_lower_bound")) is not None:
+            self.gguf_writer.add_kda_gate_lower_bound(lb)
+
+        # --- MLA ---
+        if (q_lora_rank := self.hparams.get("q_lora_rank")) is not None:
+            self.gguf_writer.add_q_lora_rank(q_lora_rank)
+        kv_lora_rank = self.hparams["kv_lora_rank"]
+        self.gguf_writer.add_kv_lora_rank(kv_lora_rank)
+
+        qk_nope_head_dim = self.hparams["qk_nope_head_dim"]
+        qk_rope_head_dim = self.hparams["qk_rope_head_dim"]
+        v_head_dim = self.hparams["v_head_dim"]
+        # K3 is nope-only; qk_rope_head_dim still sizes the un-absorbed part of K
+        assert self.hparams.get("mla_use_nope"), "K3 MLA is expected to be nope-only"
+        self.gguf_writer.add_rope_dimension_count(qk_rope_head_dim)
+        # MLA is served as MQA, so the cache holds the compressed latent
+        self.gguf_writer.add_key_length(kv_lora_rank + qk_rope_head_dim)
+        self.gguf_writer.add_value_length(kv_lora_rank)
+        self.gguf_writer.add_key_length_mla(qk_nope_head_dim + qk_rope_head_dim)
+        self.gguf_writer.add_value_length_mla(v_head_dim)
+
+        # --- MoE ---
+        self.gguf_writer.add_expert_feed_forward_length(self.hparams["moe_intermediate_size"])
+        self.gguf_writer.add_expert_shared_count(self.hparams["num_shared_experts"])
+        self.gguf_writer.add_leading_dense_block_count(self.hparams["first_k_dense_replace"])
+        self.gguf_writer.add_expert_weights_scale(self.hparams["routed_scaling_factor"])
+        self.gguf_writer.add_expert_weights_norm(self.hparams["moe_renormalize"])
+        assert self.hparams["moe_router_activation_func"] == "sigmoid"
+        self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID)
+        # latent MoE: routed experts live in a down-projected space
+        if (latent := self.hparams.get("routed_expert_hidden_size")) is not None:
+            self.gguf_writer.add_expert_latent_length(latent)
+
+        # --- situ activation ---
+        assert self.hparams["hidden_act"] == "situ", \
+            f"unexpected hidden_act {self.hparams['hidden_act']!r}"
+        self.gguf_writer.add_activation_situ_beta(self.hparams["activation_situ_beta"])
+        self.gguf_writer.add_activation_situ_linear_beta(self.hparams["activation_situ_linear_beta"])
+
+        # --- cross-layer attention residuals ---
+        self.gguf_writer.add_attn_res_block_size(self.hparams["attn_res_block_size"])
+
+    def prepare_tensors(self):
+        super().prepare_tensors()
+        if self._experts is not None:
+            leftover = [k for d in self._experts for k in d.keys()]
+            if leftover:
+                raise ValueError(f"Unprocessed experts: {leftover}")
+        if self._res_parts:
+            raise ValueError(f"Unpaired attention-residual tensors: {sorted(self._res_parts)}")
+        if self._is_mxfp4_packed():
+            # label the file for what it is; prepare_metadata runs after this
+            self._is_mxfp4 = True
+            self.ftype = gguf.LlamaFileType.MOSTLY_MXFP4_MOE
+
+    def _try_fuse_res(self, data_torch: Tensor, name: str, bid: int | None):
+        """
+        Pair <x>_res_norm.weight with <x>_res_proj.weight and emit their product.
+
+        Returns None if this is not a res tensor, [] if buffered until its pair.
+        """
+        for prefix, (tensor_id, per_layer) in self._RES_FUSIONS.items():
+            for kind in ("norm", "proj"):
+                if not name.endswith(f"{prefix}_{kind}.weight"):
+                    continue
+                key = f"{prefix}.{bid}"
+                other = self._res_parts.pop(key, None)
+                if other is None:
+                    self._res_parts[key] = (kind, data_torch)
+                    return []
+                other_kind, other_data = other
+                assert other_kind != kind, f"duplicate {kind} for {key}"
+                norm = data_torch if kind == "norm" else other_data
+                proj = data_torch if kind == "proj" else other_data
+                fused = norm.float().flatten() * proj.float().flatten()
+                # ".weight" suffix matches the convention map_tensor_name applies
+                new_name = (self.format_tensor_name(tensor_id, bid) if per_layer
+                            else gguf.TENSOR_NAMES[tensor_id] + ".weight")
+                logger.info(f"fused {prefix}_norm * {prefix}_proj -> {new_name}")
+                return [(new_name, fused)]
+        return None
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # --- cross-layer attention residuals: fuse norm * proj ---
+        fused = self._try_fuse_res(data_torch, name, bid)
+        if fused is not None:
+            yield from fused
+            return
+
+        # --- KDA conv1d: HF [d_inner, 1, d_conv] -> ggml ne [d_conv, 1, d_inner, 1] ---
+        # GGUF reverses the numpy shape on write, so target numpy (1, d_inner, 1, d_conv).
+        # conv_step varies fastest in both layouts, so this is a pure reshape.
+        if name.endswith((".q_conv1d.weight", ".k_conv1d.weight", ".v_conv1d.weight")):
+            if data_torch.ndim == 3:      # [d_inner, 1, d_conv]
+                d_inner, _, d_conv = data_torch.shape
+            elif data_torch.ndim == 2:    # [d_inner, d_conv]
+                d_inner, d_conv = data_torch.shape
+            else:
+                raise ValueError(f"unexpected conv1d rank {data_torch.ndim} for {name}")
+            data_torch = data_torch.reshape(1, d_inner, 1, d_conv)
+
+        # -exp(A_log) is folded here so the graph does not have to
+        if name.endswith(".A_log"):
+            n_head = self.hparams["num_attention_heads"]
+            data_torch = -torch.exp(data_torch.float()[:n_head])
+
+        # dt_bias -> the name SSM_DT's mapping expects
+        if name.endswith(".dt_bias"):
+            name = name.rpartition(".dt_bias")[0] + ".dt_proj.bias"
+
+        # --- g_proj is two different tensors sharing one HF name ---
+        # KDA layers: full-rank gate, [d_inner, n_embd]  (replaces g_a/g_b)
+        # MLA layers: output gate,    [n_head*v_head_dim, n_embd]
+        # Name-based mapping cannot tell them apart, so resolve by layer type.
+        if name.endswith(".self_attn.g_proj.weight"):
+            assert bid is not None
+            is_kda = (bid + 1) not in self.hparams["linear_attn_config"]["full_attn_layers"]
+            tensor_id = gguf.MODEL_TENSOR.SSM_G if is_kda else gguf.MODEL_TENSOR.ATTN_GATE
+            yield self.format_tensor_name(tensor_id, bid), data_torch
+            return
+
+        # --- routed experts: stack per-expert 2D weights into one 3D tensor ---
+        if ".block_sparse_moe.experts." in name:
+            n_experts = self.hparams["num_experts"]
+            assert bid is not None
+
+            if self._experts is None:
+                self._experts = [{} for _ in range(self.block_count)]
+            self._experts[bid][name] = data_torch
+
+            if len(self._experts[bid]) < n_experts * 3:
+                return
+
+            # w1: gate, w2: down, w3: up
+            for wid, tensor_id in (("w1", gguf.MODEL_TENSOR.FFN_GATE_EXP),
+                                   ("w2", gguf.MODEL_TENSOR.FFN_DOWN_EXP),
+                                   ("w3", gguf.MODEL_TENSOR.FFN_UP_EXP)):
+                datas = []
+                for xid in range(n_experts):
+                    ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{wid}.weight"
+                    datas.append(self._experts[bid].pop(ename))
+                stacked = torch.stack(datas, dim=0)
+                yield from super().modify_tensors(stacked, self.format_tensor_name(tensor_id, bid), bid)
+            return
+
+        # --- MLA absorption: split kv_b into k_b (transposed) and v_b ---
+        if name.endswith("kv_b_proj.weight"):
+            n_head_kv = self.hparams["num_key_value_heads"]
+            v_head_dim = self.hparams["v_head_dim"]
+            qk_nope_head_dim = self.hparams["qk_nope_head_dim"]
+            assert data_torch.shape[0] == n_head_kv * (v_head_dim + qk_nope_head_dim)
+            kv_b = data_torch.view(n_head_kv, v_head_dim + qk_nope_head_dim, data_torch.shape[-1])
+            k_b, v_b = torch.split(kv_b, [qk_nope_head_dim, v_head_dim], dim=1)
+            k_b = k_b.transpose(1, 2)
+            yield from super().modify_tensors(k_b, name.replace("kv_b_proj", "k_b_proj"), bid)
+            yield from super().modify_tensors(v_b, name.replace("kv_b_proj", "v_b_proj"), bid)
+            return
+
+        yield from super().modify_tensors(data_torch, name, bid)

conversion/minimax.py

@@ -1,13 +1,121 @@
 from __future__ import annotations
 
-from typing import TYPE_CHECKING
+from typing import Iterable, Sequence, TYPE_CHECKING
 
 import torch
 
 if TYPE_CHECKING:
     from torch import Tensor
 
-from .base import ModelBase, TextModel, MmprojModel, gguf
+from .base import ModelBase, TextModel, MmprojModel, gguf, logger
+
+
+@ModelBase.register("MiniMaxText01ForCausalLM")
+@ModelBase.register("MiniMaxM1ForCausalLM")
+class MiniMaxText01Model(TextModel):
+    model_arch = gguf.MODEL_ARCH.MINIMAX01
+
+    def _get_suppress_tokens(self) -> Sequence[int] | None:
+        import json
+        from transformers import AutoTokenizer
+        from .base import LazyTorchTensor
+
+        # check added tokens embeddings in embeddings tensor for zero-valued embeddings
+        # they get in the way of the token sampling process and must be suppressed
+
+        tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True)
+        tokenizer_vocab_size = tokenizer.vocab_size
+
+        with open(self.dir_model / "model.safetensors.index.json", "r", encoding="utf-8") as f:
+            weight_map = json.load(f)["weight_map"]
+
+        embeddings_tensor_name = "model.embed_tokens.weight"
+        embeddings_shard_name = weight_map[embeddings_tensor_name]
+        with gguf.utility.SafetensorsLocal(self.dir_model / embeddings_shard_name) as model_shard:
+            embeddings_data = model_shard[embeddings_tensor_name]
+
+        embeddings_weights_dtype = LazyTorchTensor._dtype_str_map[embeddings_data.dtype]
+        embeddings_weights = torch.from_numpy(embeddings_data.mmap_bytes()).view(embeddings_weights_dtype).reshape(embeddings_data.shape)
+        embeddings_vocab_size = embeddings_weights.shape[0]
+
+        embeddings_added_tokens = embeddings_weights[tokenizer_vocab_size:embeddings_vocab_size]
+        embeddings_zero_rows = torch.all(embeddings_added_tokens == 0, dim=1)
+        tokens_zero_embeddings_ids = (torch.nonzero(embeddings_zero_rows, as_tuple=False).flatten() + tokenizer_vocab_size).tolist()
+
+        return tokens_zero_embeddings_ids
+
+    def set_vocab(self) -> None:
+        from pathlib import Path
+
+        self._set_vocab_gpt2()
+
+        for tmpl_file in [
+            self.dir_model / "chat_template.jinja",
+            Path(__file__).parent.parent / "models" / "templates" / "MiniMax-M1.jinja"
+        ]:
+            if tmpl_file.is_file():
+                self.gguf_writer.add_chat_template(tmpl_file.read_text(encoding="utf-8"))
+                logger.info(f"Chat template overridden with {tmpl_file}.")
+                break
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+
+        suppress_tokens = self._get_suppress_tokens()
+        if suppress_tokens:
+            logger.info(f"Suppressing tokens with zero embeddings {suppress_tokens}")
+            self.gguf_writer.add_suppress_tokens(suppress_tokens)
+
+        layernorm_full_attention_alpha = self.hparams["layernorm_full_attention_alpha"]
+        layernorm_full_attention_beta = self.hparams["layernorm_full_attention_beta"]
+        layernorm_linear_attention_alpha = self.hparams["layernorm_linear_attention_alpha"]
+        layernorm_linear_attention_beta = self.hparams["layernorm_linear_attention_beta"]
+        layernorm_mlp_alpha = self.hparams["layernorm_mlp_alpha"]
+        layernorm_mlp_beta = self.hparams["layernorm_mlp_beta"]
+        assert layernorm_full_attention_alpha == layernorm_linear_attention_alpha == layernorm_mlp_alpha
+        assert layernorm_full_attention_beta == layernorm_linear_attention_beta == layernorm_mlp_beta == 1.0
+        # we do not store the layernorm betas as they are all 1.0
+        # layernorm alphas are stored as single residual_scale hparam
+        self.gguf_writer.add_residual_scale(layernorm_full_attention_alpha)
+
+        self.gguf_writer.add_rope_dimension_count(self.hparams["rotary_dim"])
+
+    _experts: list[dict[str, Tensor]] | None = None
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # process the experts separately
+        if name.find("block_sparse_moe.experts") != -1:
+            n_experts = self.hparams["num_local_experts"]
+
+            assert bid is not None
+
+            if self._experts is None:
+                self._experts = [{} for _ in range(self.block_count)]
+
+            self._experts[bid][name] = data_torch
+
+            if len(self._experts[bid]) >= n_experts * 3:
+                # merge the experts into a single 3d tensor
+                for wid in ["w1", "w2", "w3"]:
+                    datas: list[Tensor] = []
+
+                    for xid in range(n_experts):
+                        ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{wid}.weight"
+                        datas.append(self._experts[bid][ename])
+                        del self._experts[bid][ename]
+
+                    data_torch = torch.stack(datas, dim=0)
+
+                    merged_name = f"layers.{bid}.feed_forward.experts.{wid}.weight"
+
+                    new_name = self.map_tensor_name(merged_name)
+
+                    yield from super().modify_tensors(data_torch, new_name, bid)
+                return
+            else:
+                return
+
+        yield from super().modify_tensors(data_torch, name, bid)
 
 
 @ModelBase.register("MiniMaxM2ForCausalLM")

docs/backend/SYCL.md

@@ -428,13 +428,13 @@ Examples:
 - Use device 0:
 
 ```sh
-ZES_ENABLE_SYSMAN=1 ./build/bin/llama-completion -no-cnv -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 99 -sm none -mg 0 --mmap
+ZES_ENABLE_SYSMAN=1 ./build/bin/llama-completion -no-cnv -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 99 -sm none -mg 0 --load-mode auto
 ```
 
 - Use multiple devices:
 
 ```sh
-ZES_ENABLE_SYSMAN=1 ./build/bin/llama-completion -no-cnv -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 99 -sm layer --mmap
+ZES_ENABLE_SYSMAN=1 ./build/bin/llama-completion -no-cnv -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 99 -sm layer --load-mode auto
 ```
 
 *Notes:*
@@ -741,13 +741,13 @@ Examples:
 - Use device 0:
 
 ```
-build\bin\llama-completion.exe -no-cnv -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 99 -sm none -mg 0 --mmap
+build\bin\llama-completion.exe -no-cnv -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 99 -sm none -mg 0 --load-mode auto
 ```
 
 - Use multiple devices:
 
 ```
-build\bin\llama-completion.exe -no-cnv -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 99 -sm layer --mmap
+build\bin\llama-completion.exe -no-cnv -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 99 -sm layer --load-mode auto
 ```
 
 

docs/backend/snapdragon/developer.md

@@ -53,7 +53,7 @@ M=gpt-oss-20b-Q4_0.gguf NDEV=4 D=HTP0,HTP1,HTP2,HTP3 P=surfing.txt scripts/snapd
 ...
 LD_LIBRARY_PATH=/data/local/tmp/llama.cpp/lib
 ADSP_LIBRARY_PATH=/data/local/tmp/llama.cpp/lib
-GGML_HEXAGON_NDEV=4 ./bin/llama-cli --no-mmap -m /data/local/tmp/llama.cpp/../gguf/gpt-oss-20b-Q4_0.gguf
+GGML_HEXAGON_NDEV=4 ./bin/llama-cli --load-mode none -m /data/local/tmp/llama.cpp/../gguf/gpt-oss-20b-Q4_0.gguf
       -t 4 --ctx-size 8192 --batch-size 128 -ctk q8_0 -ctv q8_0 -fa on -ngl 99 --device HTP0,HTP1,HTP2,HTP3 -no-cnv -f surfing.txt
 ...
 llama_model_loader: - type  f32:  289 tensors

examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp

@@ -549,20 +549,34 @@ static void load_vocab(const char * filename, const Config * config, struct my_l
 
         const int token_idx = gguf_find_key(ctx, KV_TOKENIZER_LIST);
         GGML_ASSERT(token_idx >= 0);
-
-        const int score_idx = gguf_find_key(ctx, KV_TOKENIZER_SCORES);
-        GGML_ASSERT(score_idx >= 0);
-        const float * scores = (const float * ) gguf_get_arr_data(ctx, score_idx);
-
-        const int toktype_idx = gguf_find_key(ctx, KV_TOKENIZER_TOKEN_TYPE);
-        GGML_ASSERT(toktype_idx >= 0);
-        const int * toktypes = (const int * ) gguf_get_arr_data(ctx, toktype_idx);
+        if (gguf_get_kv_type(ctx, token_idx) != GGUF_TYPE_ARRAY ||
+            gguf_get_arr_type(ctx, token_idx) != GGUF_TYPE_STRING) {
+            die_fmt("invalid gguf type for %s", KV_TOKENIZER_LIST);
+        }
 
         const uint32_t n_vocab = gguf_get_arr_n(ctx, token_idx);
         if (n_vocab != static_cast<uint32_t>(config->vocab_size)) {
             die_fmt("vocab size mismatch: (gguf) %u != (llama2c) %d", n_vocab, config->vocab_size);
         }
 
+        const int score_idx = gguf_find_key(ctx, KV_TOKENIZER_SCORES);
+        GGML_ASSERT(score_idx >= 0);
+        if (gguf_get_kv_type(ctx, score_idx) != GGUF_TYPE_ARRAY ||
+            gguf_get_arr_type(ctx, score_idx) != GGUF_TYPE_FLOAT32 ||
+            gguf_get_arr_n(ctx, score_idx) < n_vocab) {
+            die_fmt("invalid gguf type or size for %s", KV_TOKENIZER_SCORES);
+        }
+        const float * scores = (const float * ) gguf_get_arr_data(ctx, score_idx);
+
+        const int toktype_idx = gguf_find_key(ctx, KV_TOKENIZER_TOKEN_TYPE);
+        GGML_ASSERT(toktype_idx >= 0);
+        if (gguf_get_kv_type(ctx, toktype_idx) != GGUF_TYPE_ARRAY ||
+            gguf_get_arr_type(ctx, toktype_idx) != GGUF_TYPE_INT32 ||
+            gguf_get_arr_n(ctx, toktype_idx) < n_vocab) {
+            die_fmt("invalid gguf type or size for %s", KV_TOKENIZER_TOKEN_TYPE);
+        }
+        const int * toktypes = (const int * ) gguf_get_arr_data(ctx, toktype_idx);
+
         vocab->id_to_token.resize(n_vocab);
 
         for (uint32_t i = 0; i < n_vocab; i++) {

examples/sycl/run-llama2.sh

@@ -18,7 +18,7 @@ CONTEXT=4096
 #support malloc device memory more than 4GB.
 export UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS=1
 
-LOAD_MODE='--mmap'
+LOAD_MODE='--load-mode auto'
 if [ $# -gt 0 ]; then
     GGML_SYCL_DEVICE=$1
     echo "use $GGML_SYCL_DEVICE as main GPU"

examples/sycl/start-svr.sh

@@ -124,7 +124,7 @@ else
     GPUS_SETTING="-sm ${SPLIT_MODE}"
 fi
 
-echo "run cmd: ZES_ENABLE_SYSMAN=1 ${BIN_FILE} -m ${MODEL_FILE} -ngl ${NGL} -s ${SEED} -c ${CONTEXT} ${GPUS_SETTING} -lv ${LOG_VERBOSE} --device ${SYCL_DEVICES} --mmap --host 0.0.0.0 --port 8000"
-ZES_ENABLE_SYSMAN=1 ${BIN_FILE} -m ${MODEL_FILE} -ngl ${NGL} -s ${SEED} -c ${CONTEXT} ${GPUS_SETTING} -lv ${LOG_VERBOSE} --device ${SYCL_DEVICES} --mmap --host 0.0.0.0 --port 8000
+echo "run cmd: ZES_ENABLE_SYSMAN=1 ${BIN_FILE} -m ${MODEL_FILE} -ngl ${NGL} -s ${SEED} -c ${CONTEXT} ${GPUS_SETTING} -lv ${LOG_VERBOSE} --device ${SYCL_DEVICES} --load-mode auto --host 0.0.0.0 --port 8000"
+ZES_ENABLE_SYSMAN=1 ${BIN_FILE} -m ${MODEL_FILE} -ngl ${NGL} -s ${SEED} -c ${CONTEXT} ${GPUS_SETTING} -lv ${LOG_VERBOSE} --device ${SYCL_DEVICES} --load-mode auto --host 0.0.0.0 --port 8000
 
 

examples/sycl/test.sh

@@ -133,6 +133,6 @@ else
     GPUS_SETTING="-sm ${SPLIT_MODE}"
  fi
 
-echo "run cmd: ZES_ENABLE_SYSMAN=1 ${BIN_FILE} -m ${MODEL_FILE} -no-cnv -p "${INPUT_PROMPT}" -n 200 -e -ngl ${NGL} -s ${SEED} -c ${CONTEXT} ${GPUS_SETTING} -lv ${LOG_VERBOSE} --device ${SYCL_DEVICES} --mmap "
-ZES_ENABLE_SYSMAN=1 ${BIN_FILE} -m ${MODEL_FILE} -no-cnv -p "${INPUT_PROMPT}" -n 200 -e -ngl ${NGL} -s ${SEED} -c ${CONTEXT} ${GPUS_SETTING} -lv ${LOG_VERBOSE} --device ${SYCL_DEVICES} --mmap
+echo "run cmd: ZES_ENABLE_SYSMAN=1 ${BIN_FILE} -m ${MODEL_FILE} -no-cnv -p "${INPUT_PROMPT}" -n 200 -e -ngl ${NGL} -s ${SEED} -c ${CONTEXT} ${GPUS_SETTING} -lv ${LOG_VERBOSE} --device ${SYCL_DEVICES} --load-mode auto "
+ZES_ENABLE_SYSMAN=1 ${BIN_FILE} -m ${MODEL_FILE} -no-cnv -p "${INPUT_PROMPT}" -n 200 -e -ngl ${NGL} -s ${SEED} -c ${CONTEXT} ${GPUS_SETTING} -lv ${LOG_VERBOSE} --device ${SYCL_DEVICES} --load-mode auto
 

examples/sycl/win-run-llama2.bat

@@ -7,5 +7,5 @@ set INPUT2="Building a website can be done in 10 simple steps:\nStep 1:"
 
 :: support malloc device memory more than 4GB.
 set UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS=1
-set LOAD_MODE="--mmap"
+set LOAD_MODE="--load-mode auto"
 .\build\bin\llama-completion.exe -m models\llama-2-7b.Q4_0.gguf -no-cnv -p %INPUT2% -n 400 -e -ngl 99 -s 0 %LOAD_MODE%

examples/sycl/win-start-svr.bat

@@ -188,9 +188,9 @@ if not "%GGML_SYCL_DEVICE%"=="-1" (
   set "GPUS_SETTING=-sm %SPLIT_MODE%"
 )
 
-echo run cmd: ZES_ENABLE_SYSMAN=1 %BIN_FILE% -m "%MODEL_FILE%" -ngl %NGL% -s %SEED% -c %CONTEXT% %GPUS_SETTING% -lv %LOG_VERBOSE% --device %SYCL_DEVICES% --mmap --host 0.0.0.0 --port 8000
+echo run cmd: ZES_ENABLE_SYSMAN=1 %BIN_FILE% -m "%MODEL_FILE%" -ngl %NGL% -s %SEED% -c %CONTEXT% %GPUS_SETTING% -lv %LOG_VERBOSE% --device %SYCL_DEVICES% --load-mode auto --host 0.0.0.0 --port 8000
 set "ZES_ENABLE_SYSMAN=1"
-%BIN_FILE% -m "%MODEL_FILE%" -ngl %NGL% -s %SEED% -c %CONTEXT% %GPUS_SETTING% -lv %LOG_VERBOSE% --device "%SYCL_DEVICES%" --mmap --host 0.0.0.0 --port 8000
+%BIN_FILE% -m "%MODEL_FILE%" -ngl %NGL% -s %SEED% -c %CONTEXT% %GPUS_SETTING% -lv %LOG_VERBOSE% --device "%SYCL_DEVICES%" --load-mode auto --host 0.0.0.0 --port 8000
 
 endlocal
 

examples/sycl/win-test.bat

@@ -211,9 +211,9 @@ else (
   set "GPUS_SETTING=-sm %SPLIT_MODE%"
 )
 
-echo run cmd: ZES_ENABLE_SYSMAN=1 %BIN_FILE% -m %MODEL_FILE% -no-cnv -p "%INPUT_PROMPT%" -n 200 -e -ngl %NGL% -s %SEED% -c %CONTEXT% %GPUS_SETTING% -lv %LOG_VERBOSE% --device %SYCL_DEVICES% --mmap
+echo run cmd: ZES_ENABLE_SYSMAN=1 %BIN_FILE% -m %MODEL_FILE% -no-cnv -p "%INPUT_PROMPT%" -n 200 -e -ngl %NGL% -s %SEED% -c %CONTEXT% %GPUS_SETTING% -lv %LOG_VERBOSE% --device %SYCL_DEVICES% --load-mode auto
 set "ZES_ENABLE_SYSMAN=1"
-%BIN_FILE% -m "%MODEL_FILE%" -no-cnv -p "%INPUT_PROMPT%" -n 200 -e -ngl %NGL% -s %SEED% -c %CONTEXT% %GPUS_SETTING% -lv %LOG_VERBOSE% --device "%SYCL_DEVICES%" --mmap
+%BIN_FILE% -m "%MODEL_FILE%" -no-cnv -p "%INPUT_PROMPT%" -n 200 -e -ngl %NGL% -s %SEED% -c %CONTEXT% %GPUS_SETTING% -lv %LOG_VERBOSE% --device "%SYCL_DEVICES%" --load-mode auto
 
 endlocal
 

ggml/CMakeLists.txt

@@ -4,7 +4,7 @@ project("ggml" C CXX ASM)
 
 ### GGML Version
 set(GGML_VERSION_MAJOR 0)
-set(GGML_VERSION_MINOR 19)
+set(GGML_VERSION_MINOR 20)
 set(GGML_VERSION_PATCH 0)
 set(GGML_VERSION_BASE "${GGML_VERSION_MAJOR}.${GGML_VERSION_MINOR}.${GGML_VERSION_PATCH}")
 

ggml/src/ggml-vulkan/ggml-vulkan.cpp

@@ -2066,7 +2066,7 @@ struct ggml_vk_garbage_collector {
 static void ggml_vk_preallocate_buffers(ggml_backend_vk_context * ctx, vk_context subctx);
 static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested = nullptr);
 static void ggml_pipeline_allocate_descriptor_sets(ggml_backend_vk_context * ctx);
-static bool ggml_vk_intel_windows_driver_equals_or_newer_than(uint32_t driver_version, uint32_t threshold_major, uint32_t threshold_minor);
+static bool ggml_vk_intel_windows_driver_in_range(uint32_t driver_version, uint32_t lower_major, uint32_t lower_minor, uint32_t upper_major, uint32_t upper_minor);
 
 static bool vk_memory_logger_enabled = false;
 
@@ -3962,7 +3962,10 @@ static bool ggml_vk_matmul_shmem_support(const vk_device& device, const std::vec
     }
 
     // Needs to be kept up to date on shader changes
-    const uint32_t bank_conflict_offset = device->coopmat_support ? 8 : 1;
+    // Needs to stay aligned with ggml_vk_mul_mm_spec.
+    const bool intel_shmem_stride_pad_zero = device->vendor_id == VK_VENDOR_ID_INTEL && device->coopmat_support &&
+                                              device->driver_id == vk::DriverId::eIntelProprietaryWindows;
+    const uint32_t bank_conflict_offset = intel_shmem_stride_pad_zero ? 0 : (device->coopmat_support ? 8 : 1);
     const uint32_t type_size = device->fp16 ? sizeof(ggml_fp16_t) : sizeof(float);
     const uint32_t warps = warptile[0] / warptile[10];
 
@@ -4579,8 +4582,13 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) {
     }
 #endif
 
-    auto const &ggml_vk_mul_mm_spec = [](std::vector<uint32_t> spec, bool aligned) {
-        spec.push_back(aligned ? 1u : 0u);
+    auto const &ggml_vk_mul_mm_spec = [&device](std::vector<uint32_t> spec, bool aligned) {
+        spec.push_back(aligned ? 1u : 0u);  // constantID=11: ALIGNED
+        if (device->vendor_id == VK_VENDOR_ID_INTEL && device->coopmat_support &&
+            device->driver_id == vk::DriverId::eIntelProprietaryWindows) {
+            spec.push_back(0u);  // constantID=12: SHMEM_STRIDE_PAD = 0
+            spec.push_back(1u);  // constantID=13: APPLY_SLM_A_RESHAPE = true
+        }
         return spec;
     };
 
@@ -5742,10 +5750,9 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) {
     ggml_vk_create_pipeline(device, device->pipeline_argmax_f32, "argmax_f32", argmax_f32_len, argmax_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
 
     ggml_vk_create_pipeline(device, device->pipeline_sum_rows_f32, "sum_rows_f32", sum_rows_f32_len, sum_rows_f32_data, "main", 2, sizeof(vk_op_sum_rows_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
-    // Intel Windows driver older than 32.0.101.8860 will crash when using fwht kernels on Xe2+ GPUS so we gate that here
+    // Intel Windows driver in range [32.0.101.8509, 32.0.101.8860) will crash when using fwht kernels so we gate that here
     const bool can_use_fwht = device->driver_id != vk::DriverId::eIntelProprietaryWindows ||
-        device->architecture != vk_device_architecture::INTEL_XE2 ||
-        (device->architecture == vk_device_architecture::INTEL_XE2 && ggml_vk_intel_windows_driver_equals_or_newer_than(device->properties.driverVersion, 101, 8860));
+        !ggml_vk_intel_windows_driver_in_range(device->properties.driverVersion, 101, 8509, 101, 8860);
     if (can_use_fwht && device->subgroup_basic && device->subgroup_shuffle) {
         int idx = 0;
         for (uint32_t n : {64, 128, 256, 512}) {
@@ -18871,17 +18878,23 @@ static uint32_t ggml_vk_intel_shader_core_count(const vk::PhysicalDevice& vkdev)
     }
 }
 
-static bool ggml_vk_intel_windows_driver_equals_or_newer_than(uint32_t driver_version, uint32_t threshold_major, uint32_t threshold_minor) {
+// checks whether lower <= driver_version < upper, with each bound given as xxx.yyyy
+static bool ggml_vk_intel_windows_driver_in_range(uint32_t driver_version, uint32_t lower_major, uint32_t lower_minor, uint32_t upper_major, uint32_t upper_minor) {
 #if defined(_WIN32)
     // Intel Windows encodes xxx.yyyy as [31:14].[13:0].
     const uint32_t major = driver_version >> 14;
     const uint32_t minor = driver_version & 0x3fff;
 
-    return major > threshold_major || (major == threshold_major && minor >= threshold_minor);
+    const bool ge_lower = major > lower_major || (major == lower_major && minor >= lower_minor);
+    const bool lt_upper = major < upper_major || (major == upper_major && minor < upper_minor);
+
+    return ge_lower && lt_upper;
 #else
     GGML_UNUSED(driver_version);
-    GGML_UNUSED(threshold_major);
-    GGML_UNUSED(threshold_minor);
+    GGML_UNUSED(lower_major);
+    GGML_UNUSED(lower_minor);
+    GGML_UNUSED(upper_major);
+    GGML_UNUSED(upper_minor);
     return true;
 #endif
 }

ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp

@@ -119,10 +119,13 @@ layout (constant_id = 3) const uint BK = 16;  // Assumed to be 32 if working wit
 #endif
 
 #ifdef COOPMAT
-#define SHMEM_STRIDE (BK / 2 + 4)
+layout(constant_id = 12) const uint SHMEM_STRIDE_PAD = 4;
+layout(constant_id = 13) const bool APPLY_SLM_A_RESHAPE = false;
 #else
-#define SHMEM_STRIDE (BK / 2 + 1)
+const uint SHMEM_STRIDE_PAD = 1;
+const bool APPLY_SLM_A_RESHAPE = false;
 #endif
+#define SHMEM_STRIDE (BK / 2 + SHMEM_STRIDE_PAD)
 
 shared FLOAT_TYPEV2 buf_a[BM * SHMEM_STRIDE];
 shared FLOAT_TYPEV2 buf_b[BN * SHMEM_STRIDE];
@@ -302,7 +305,7 @@ void main() {
         [[unroll]] for (uint i = 0; i < BK; i += TK) {
             [[unroll]] for (uint cm_row = 0; cm_row < cms_per_row; cm_row++) {
                 // Load from shared into cache
-                coopMatLoad(cache_a, buf_a, (warp_r * WM + cm_row * TM) * SHMEM_STRIDE + i / 2, SHMEM_STRIDE, gl_CooperativeMatrixLayoutRowMajor);
+                coopMatLoad(cache_a, buf_a, a_shmem_index(warp_r * WM + cm_row * TM, i / 2), a_shmem_stride(), gl_CooperativeMatrixLayoutRowMajor);
 
                 [[unroll]] for (uint cm_col = 0; cm_col < cms_per_col; cm_col++) {
                     coopMatLoad(cache_b, buf_b, (warp_c * WN + cm_col * TN) * SHMEM_STRIDE + i / 2, SHMEM_STRIDE, gl_CooperativeMatrixLayoutColumnMajor);

ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_funcs.glsl

@@ -1,60 +1,76 @@
+// k_pair is the K coordinate measured in FLOAT_TYPEV2 elements.
+uint a_shmem_index(uint m, uint k_pair) {
+    if (APPLY_SLM_A_RESHAPE) {
+        const uint tile_width = TK / 2;
+        return (k_pair / tile_width) * BM * tile_width
+             + m * tile_width
+             + k_pair % tile_width;
+    }
+    return m * SHMEM_STRIDE + k_pair;
+}
+
+uint a_shmem_stride() {
+    return APPLY_SLM_A_RESHAPE ? TK / 2 : SHMEM_STRIDE;
+}
+
+void store_a(uint m, uint k_pair, FLOAT_TYPEV2 value) {
+    buf_a[a_shmem_index(m, k_pair)] = value;
+}
+
 void load_a_to_shmem(const uint pos_a, const uint row, const uint col, const uint idx_m, const uint block, const uint end_k) {
 #if defined(DATA_A_F32) || defined(DATA_A_F16)
 #if LOAD_VEC_A == 8
             if (ALIGNED != 0) {
                 const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row;
-                const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 2;
+                const uint k_pair = row * LOAD_VEC_A / 2;
                 FLOAT_TYPEV8 aa = FLOAT_TYPEV8(data_a[idx]);
-                buf_a[buf_idx    ] = aa[0].xy;
-                buf_a[buf_idx + 1] = aa[0].zw;
-                buf_a[buf_idx + 2] = aa[1].xy;
-                buf_a[buf_idx + 3] = aa[1].zw;
+                store_a(col, k_pair,     aa[0].xy);
+                store_a(col, k_pair + 1, aa[0].zw);
+                store_a(col, k_pair + 2, aa[1].xy);
+                store_a(col, k_pair + 3, aa[1].zw);
                 return;
             }
 #elif LOAD_VEC_A == 4
             if (ALIGNED != 0) {
                 const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row;
-                const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 2;
+                const uint k_pair = row * LOAD_VEC_A / 2;
                 FLOAT_TYPEV4 aa = FLOAT_TYPEV4(data_a[idx]);
-                buf_a[buf_idx    ] = aa.xy;
-                buf_a[buf_idx + 1] = aa.zw;
+                store_a(col, k_pair,     aa.xy);
+                store_a(col, k_pair + 1, aa.zw);
                 return;
             }
 #endif
             const uint idx = pos_a + col * p.stride_a + row * 2;
-            const uint buf_idx = col * SHMEM_STRIDE + row;
             if (idx_m < p.M && block + row * 2 + 1 < end_k) {
-                buf_a[buf_idx] = FLOAT_TYPEV2(data_a_scalar[idx],
-                                              data_a_scalar[idx + 1]);
+                store_a(col, row, FLOAT_TYPEV2(data_a_scalar[idx],
+                                               data_a_scalar[idx + 1]));
             } else if (idx_m < p.M && block + row * 2 < end_k) {
-                buf_a[buf_idx] = FLOAT_TYPEV2(data_a_scalar[idx], 0.0f);
+                store_a(col, row, FLOAT_TYPEV2(data_a_scalar[idx], 0.0f));
             } else {
-                buf_a[buf_idx] = FLOAT_TYPEV2(0.0f);
+                store_a(col, row, FLOAT_TYPEV2(0.0f));
             }
 #elif defined(DATA_A_BF16)
 #if LOAD_VEC_A == 4
             if (ALIGNED != 0) {
                 const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row;
-                const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 2;
+                const uint k_pair = row * LOAD_VEC_A / 2;
                 FLOAT_TYPEV4 aa = FLOAT_TYPEV4(TO_FLOAT_TYPE(data_a[idx]));
-                buf_a[buf_idx    ] = aa.xy;
-                buf_a[buf_idx + 1] = aa.zw;
+                store_a(col, k_pair,     aa.xy);
+                store_a(col, k_pair + 1, aa.zw);
                 return;
             }
 #endif
             const uint idx = pos_a + col * p.stride_a + row * 2;
-            const uint buf_idx = col * SHMEM_STRIDE + row;
             if (idx_m < p.M && block + row * 2 + 1 < end_k) {
-                buf_a[buf_idx] = FLOAT_TYPEV2(TO_FLOAT_TYPE(data_a_scalar[idx]),
-                                              TO_FLOAT_TYPE(data_a_scalar[idx + 1]));
+                store_a(col, row, FLOAT_TYPEV2(TO_FLOAT_TYPE(data_a_scalar[idx]),
+                                               TO_FLOAT_TYPE(data_a_scalar[idx + 1])));
             } else if (idx_m < p.M && block + row * 2 < end_k) {
-                buf_a[buf_idx] = FLOAT_TYPEV2(TO_FLOAT_TYPE(data_a_scalar[idx]), 0.0f);
+                store_a(col, row, FLOAT_TYPEV2(TO_FLOAT_TYPE(data_a_scalar[idx]), 0.0f));
             } else {
-                buf_a[buf_idx] = FLOAT_TYPEV2(0.0f);
+                store_a(col, row, FLOAT_TYPEV2(0.0f));
             }
 #elif defined(DATA_A_Q4_0)
             const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row;
-            const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 4;
 
             const uint ib = idx / 4;
             const uint iqs = idx & 0x03;
@@ -64,13 +80,13 @@ void load_a_to_shmem(const uint pos_a, const uint row, const uint col, const uin
             const vec4 v0 = (vec4(unpack8(vui & 0x0F0F0F0F)) - 8.0f) * d;
             const vec4 v1 = (vec4(unpack8((vui >> 4) & 0x0F0F0F0F)) - 8.0f) * d;
 
-            buf_a[buf_idx    ] = FLOAT_TYPEV2(v0.xy);
-            buf_a[buf_idx + 1] = FLOAT_TYPEV2(v0.zw);
-            buf_a[buf_idx + 8] = FLOAT_TYPEV2(v1.xy);
-            buf_a[buf_idx + 9] = FLOAT_TYPEV2(v1.zw);
+            const uint k_pair = row * LOAD_VEC_A / 4;
+            store_a(col, k_pair,     FLOAT_TYPEV2(v0.xy));
+            store_a(col, k_pair + 1, FLOAT_TYPEV2(v0.zw));
+            store_a(col, k_pair + 8, FLOAT_TYPEV2(v1.xy));
+            store_a(col, k_pair + 9, FLOAT_TYPEV2(v1.zw));
 #elif defined(DATA_A_Q4_1)
             const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row;
-            const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 4;
 
             const uint ib = idx / 4;
             const uint iqs = idx & 0x03;
@@ -80,13 +96,13 @@ void load_a_to_shmem(const uint pos_a, const uint row, const uint col, const uin
             const vec4 v0 = vec4(unpack8(vui & 0x0F0F0F0F)) * dm.x + dm.y;
             const vec4 v1 = vec4(unpack8((vui >> 4) & 0x0F0F0F0F)) * dm.x + dm.y;
 
-            buf_a[buf_idx     ] = FLOAT_TYPEV2(v0.xy);
-            buf_a[buf_idx + 1 ] = FLOAT_TYPEV2(v0.zw);
-            buf_a[buf_idx + 8 ] = FLOAT_TYPEV2(v1.xy);
-            buf_a[buf_idx + 9 ] = FLOAT_TYPEV2(v1.zw);
+            const uint k_pair = row * LOAD_VEC_A / 4;
+            store_a(col, k_pair,     FLOAT_TYPEV2(v0.xy));
+            store_a(col, k_pair + 1, FLOAT_TYPEV2(v0.zw));
+            store_a(col, k_pair + 8, FLOAT_TYPEV2(v1.xy));
+            store_a(col, k_pair + 9, FLOAT_TYPEV2(v1.zw));
 #elif defined(DATA_A_Q5_0)
             const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row;
-            const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 4;
 
             const uint ib = idx / 8;
             const uint iqs = idx & 0x07;
@@ -98,12 +114,10 @@ void load_a_to_shmem(const uint pos_a, const uint row, const uint col, const uin
 
             const uint vui = uint(data_a_packed16[ib].qs[iqs]);
             const vec4 v = (vec4((vui & 0xF) | qh0.x, ((vui >> 4) & 0xF) | qh0.y, ((vui >> 8) & 0xF) | qh1.x, (vui >> 12) | qh1.y) - 16.0f) * d;
-
-            buf_a[buf_idx    ] = FLOAT_TYPEV2(v.xz);
-            buf_a[buf_idx + 8] = FLOAT_TYPEV2(v.yw);
+            store_a(col, row,     FLOAT_TYPEV2(v.xz));
+            store_a(col, row + 8, FLOAT_TYPEV2(v.yw));
 #elif defined(DATA_A_Q5_1)
             const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row;
-            const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 4;
 
             const uint ib = idx / 4;
             const uint iqs = idx & 0x03;
@@ -119,13 +133,13 @@ void load_a_to_shmem(const uint pos_a, const uint row, const uint col, const uin
             const vec4 v0 = vec4((vui & 0xF) | qh0.x, ((vui >> 4) & 0xF) | qh0.y, ((vui >> 8) & 0xF) | qh1.x, ((vui >> 12) & 0xF) | qh1.y) * dm.x + dm.y;
             const vec4 v1 = vec4(((vui >> 16) & 0xF) | qh2.x, ((vui >> 20) & 0xF) | qh2.y, ((vui >> 24) & 0xF) | qh3.x, ((vui >> 28) & 0xF) | qh3.y) * dm.x + dm.y;
 
-            buf_a[buf_idx    ] = FLOAT_TYPEV2(v0.xz);
-            buf_a[buf_idx + 1] = FLOAT_TYPEV2(v1.xz);
-            buf_a[buf_idx + 8] = FLOAT_TYPEV2(v0.yw);
-            buf_a[buf_idx + 9] = FLOAT_TYPEV2(v1.yw);
+            const uint k_pair = row * LOAD_VEC_A / 4;
+            store_a(col, k_pair,     FLOAT_TYPEV2(v0.xz));
+            store_a(col, k_pair + 1, FLOAT_TYPEV2(v1.xz));
+            store_a(col, k_pair + 8, FLOAT_TYPEV2(v0.yw));
+            store_a(col, k_pair + 9, FLOAT_TYPEV2(v1.yw));
 #elif defined(DATA_A_Q8_0)
             const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row;
-            const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 2;
 
             const uint ib = idx / 8;
             const uint iqs = idx & 0x07;
@@ -135,11 +149,11 @@ void load_a_to_shmem(const uint pos_a, const uint row, const uint col, const uin
             const i8vec2 v1 = unpack8(int32_t(data_a_packed16[ib].qs[2*iqs + 1])).xy;
             const vec4 v = vec4(v0.x, v0.y, v1.x, v1.y) * d;
 
-            buf_a[buf_idx    ] = FLOAT_TYPEV2(v.xy);
-            buf_a[buf_idx + 1] = FLOAT_TYPEV2(v.zw);
+            const uint k_pair = row * LOAD_VEC_A / 2;
+            store_a(col, k_pair,     FLOAT_TYPEV2(v.xy));
+            store_a(col, k_pair + 1, FLOAT_TYPEV2(v.zw));
 #elif defined(DATA_A_Q1_0)
             const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row;
-            const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 2;
 
             const uint ib = idx / 16;
             const uint iqs = idx & 0xfu;
@@ -147,13 +161,13 @@ void load_a_to_shmem(const uint pos_a, const uint row, const uint col, const uin
             const float d = float(data_a[ib].d);
             const uint bits = uint(data_a[ib].qs[iqs]);
 
-            buf_a[buf_idx    ] = FLOAT_TYPEV2((bits & 0x01u) != 0u ? d : -d, (bits & 0x02u) != 0u ? d : -d);
-            buf_a[buf_idx + 1] = FLOAT_TYPEV2((bits & 0x04u) != 0u ? d : -d, (bits & 0x08u) != 0u ? d : -d);
-            buf_a[buf_idx + 2] = FLOAT_TYPEV2((bits & 0x10u) != 0u ? d : -d, (bits & 0x20u) != 0u ? d : -d);
-            buf_a[buf_idx + 3] = FLOAT_TYPEV2((bits & 0x40u) != 0u ? d : -d, (bits & 0x80u) != 0u ? d : -d);
+            const uint k_pair = row * LOAD_VEC_A / 2;
+            store_a(col, k_pair,     FLOAT_TYPEV2((bits & 0x01u) != 0u ? d : -d, (bits & 0x02u) != 0u ? d : -d));
+            store_a(col, k_pair + 1, FLOAT_TYPEV2((bits & 0x04u) != 0u ? d : -d, (bits & 0x08u) != 0u ? d : -d));
+            store_a(col, k_pair + 2, FLOAT_TYPEV2((bits & 0x10u) != 0u ? d : -d, (bits & 0x20u) != 0u ? d : -d));
+            store_a(col, k_pair + 3, FLOAT_TYPEV2((bits & 0x40u) != 0u ? d : -d, (bits & 0x80u) != 0u ? d : -d));
 #elif defined(DATA_A_Q2_0)
             const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row;
-            const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 2;
 
             const uint ib = idx / 16;
             const uint iqs = idx & 0xfu;
@@ -161,11 +175,11 @@ void load_a_to_shmem(const uint pos_a, const uint row, const uint col, const uin
             const FLOAT_TYPE d = FLOAT_TYPE(data_a[ib].d);
             const uint bits = uint(data_a[ib].qs[iqs]);
 
-            buf_a[buf_idx    ] = d * (FLOAT_TYPEV2(bits & 3u, (bits >> 2u) & 3u) - FLOAT_TYPEV2(1.0f));
-            buf_a[buf_idx + 1] = d * (FLOAT_TYPEV2((bits >> 4u) & 3u, bits >> 6u) - FLOAT_TYPEV2(1.0f));
+            const uint k_pair = row * LOAD_VEC_A / 2;
+            store_a(col, k_pair,     d * (FLOAT_TYPEV2(bits & 3u, (bits >> 2u) & 3u) - FLOAT_TYPEV2(1.0f)));
+            store_a(col, k_pair + 1, d * (FLOAT_TYPEV2((bits >> 4u) & 3u, bits >> 6u) - FLOAT_TYPEV2(1.0f)));
 #elif defined(DATA_A_Q2_K)
             const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row;
-            const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 2;
 
             const uint ib = idx / 64;                          // 4 values per idx
             const uint iqs = (idx % 64) * 2;                   // 0,2,4..126
@@ -180,11 +194,11 @@ void load_a_to_shmem(const uint pos_a, const uint row, const uint col, const uin
 
             const vec4 v = dm.x * float(scales & 0xF) * qs - dm.y * float(scales >> 4);
 
-            buf_a[buf_idx    ] = FLOAT_TYPEV2(v.xy);
-            buf_a[buf_idx + 1] = FLOAT_TYPEV2(v.zw);
+            const uint k_pair = row * LOAD_VEC_A / 2;
+            store_a(col, k_pair,     FLOAT_TYPEV2(v.xy));
+            store_a(col, k_pair + 1, FLOAT_TYPEV2(v.zw));
 #elif defined(DATA_A_TQ2_0)
             const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row;
-            const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 2;
 
             const uint ib = idx / 128;                         // 2 values per idx
             const uint iqs = (idx % 128) * 2;                  // elem 0,2,4..254
@@ -197,10 +211,10 @@ void load_a_to_shmem(const uint pos_a, const uint row, const uint col, const uin
 
             const vec2 v = d * (vec2((qs >> shift) & 3) - 1.0);
 
-            buf_a[buf_idx] = FLOAT_TYPEV2(v.xy);
+            const uint k_pair = row * LOAD_VEC_A / 2;
+            store_a(col, k_pair, FLOAT_TYPEV2(v.xy));
 #elif defined(DATA_A_Q3_K)
             const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row;
-            const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 2;
 
             const uint ib = idx / 128;                   // 2 values per idx
             const uint iqs = idx % 128;                  // 0..127
@@ -220,11 +234,10 @@ void load_a_to_shmem(const uint pos_a, const uint row, const uint col, const uin
             const vec2 qs = vec2(unpack8((uint(data_a_packed16[ib].qs[qsi / 2]) >> qsshift) & 0x0303).xy);
             const vec2 hm = vec2(unpack8(((uint(data_a_packed16[ib].hmask[hmi / 2]) >> (4 * n + halfsplit)) & 0x0101 ^ 0x0101) << 2).xy);
 
-            buf_a[buf_idx] = FLOAT_TYPEV2(dl * (qs.x - hm.x),
-                                          dl * (qs.y - hm.y));
+            store_a(col, row * LOAD_VEC_A / 2, FLOAT_TYPEV2(dl * (qs.x - hm.x),
+                                                              dl * (qs.y - hm.y)));
 #elif defined(DATA_A_Q4_K)
             const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row;
-            const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 2;
 
             const uint ib = idx / 64;                  // 4 values per idx
             const uint iqs = (idx % 64) * 2;           // 0,2,4..126
@@ -256,11 +269,11 @@ void load_a_to_shmem(const uint pos_a, const uint row, const uint col, const uin
 
             const vec4 q = vec4(unpack8((data_a_packed32[ib].qs[qsi / 4] >> (b * 4)) & 0x0F0F0F0F));
 
-            buf_a[buf_idx    ] = FLOAT_TYPEV2(fma(d, q.x, m), fma(d, q.y, m));
-            buf_a[buf_idx + 1] = FLOAT_TYPEV2(fma(d, q.z, m), fma(d, q.w, m));
+            const uint k_pair = row * LOAD_VEC_A / 2;
+            store_a(col, k_pair,     FLOAT_TYPEV2(fma(d, q.x, m), fma(d, q.y, m)));
+            store_a(col, k_pair + 1, FLOAT_TYPEV2(fma(d, q.z, m), fma(d, q.w, m)));
 #elif defined(DATA_A_Q5_K)
             const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row;
-            const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 2;
 
             const uint ib = idx / 64;                  // 4 values per idx
             const uint iqs = (idx % 64) * 2;           // 0,2,4..126
@@ -295,11 +308,11 @@ void load_a_to_shmem(const uint pos_a, const uint row, const uint col, const uin
             const uint qh = ((data_a_packed32[ib].qh[qhi / 4] >> (iqs / 16)) & 0x01010101) << 4;
             const vec4 q = vec4(unpack8(qs | qh));
 
-            buf_a[buf_idx    ] = FLOAT_TYPEV2(fma(d, q.x, m), fma(d, q.y, m));
-            buf_a[buf_idx + 1] = FLOAT_TYPEV2(fma(d, q.z, m), fma(d, q.w, m));
+            const uint k_pair = row * LOAD_VEC_A / 2;
+            store_a(col, k_pair,     FLOAT_TYPEV2(fma(d, q.x, m), fma(d, q.y, m)));
+            store_a(col, k_pair + 1, FLOAT_TYPEV2(fma(d, q.z, m), fma(d, q.w, m)));
 #elif defined(DATA_A_Q6_K)
             const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row;
-            const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 2;
 
             const uint ib = idx / 128;                  // 2 values per idx
             const uint iqs = idx % 128;                 // 0..127
@@ -318,10 +331,9 @@ void load_a_to_shmem(const uint pos_a, const uint row, const uint col, const uin
             const uint qh = (uint(data_a_packed16[ib].qh[qhi]) >> qhshift) & 0x0303;
             const vec2 q = (vec2(unpack8(ql | (qh << 4)).xy) - 32) * dscale;
 
-            buf_a[buf_idx] = FLOAT_TYPEV2(q.x, q.y);
+            store_a(col, row * LOAD_VEC_A / 2, FLOAT_TYPEV2(q.x, q.y));
 #elif defined(DATA_A_IQ1_S)
             const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row;
-            const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 2;
 
             const uint ib = idx / 32;                  // 8 values per idx
             const uint ib32 = (idx % 32) / 4;         // 0..7
@@ -334,13 +346,13 @@ void load_a_to_shmem(const uint pos_a, const uint row, const uint col, const uin
             const float delta = ((qh & 0x8000) != 0) ? -IQ1S_DELTA : IQ1S_DELTA;
             const int16_t grid = int16_t(iq1s_grid[qs | (bitfieldExtract(qh, 3 * int(ib8 & 3), 3) << 8)]);
 
+            const uint k_pair = row * LOAD_VEC_A / 2;
             [[unroll]] for (int k = 0; k < 4; ++k) {
-                buf_a[buf_idx + k] = FLOAT_TYPEV2(dl * (bitfieldExtract(grid, 4 * k    , 2) + delta),
-                                                  dl * (bitfieldExtract(grid, 4 * k + 2, 2) + delta));
+                store_a(col, k_pair + k, FLOAT_TYPEV2(dl * (bitfieldExtract(grid, 4 * k    , 2) + delta),
+                                                       dl * (bitfieldExtract(grid, 4 * k + 2, 2) + delta)));
             }
 #elif defined(DATA_A_IQ1_M)
             const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row;
-            const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 2;
 
             const uint ib = idx / 32;  // 8 values per idx
             const uint ib8 = idx % 32;
@@ -356,13 +368,13 @@ void load_a_to_shmem(const uint pos_a, const uint row, const uint col, const uin
             const float delta = ((qh & 8) != 0) ? -IQ1M_DELTA : IQ1M_DELTA;
             const int16_t grid = int16_t(iq1s_grid[qs | ((qh & 7) << 8)]);
 
+            const uint k_pair = row * LOAD_VEC_A / 2;
             [[unroll]] for (int k = 0; k < 4; ++k) {
-                buf_a[buf_idx + k] = FLOAT_TYPEV2(dl * (bitfieldExtract(grid, 4 * k    , 2) + delta),
-                                                  dl * (bitfieldExtract(grid, 4 * k + 2, 2) + delta));
+                store_a(col, k_pair + k, FLOAT_TYPEV2(dl * (bitfieldExtract(grid, 4 * k    , 2) + delta),
+                                                       dl * (bitfieldExtract(grid, 4 * k + 2, 2) + delta)));
             }
 #elif defined(DATA_A_IQ2_XXS)
             const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row;
-            const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 2;
 
             const uint ib = idx / 32;                 // 8 values per idx
             const uint ib32 = (idx % 32) / 4;         // 0..7
@@ -383,17 +395,17 @@ void load_a_to_shmem(const uint pos_a, const uint row, const uint col, const uin
             const vec4 grid0 = vec4(unpack8(grid.x));
             const vec4 grid1 = vec4(unpack8(grid.y));
 
-            buf_a[buf_idx    ] = db * FLOAT_TYPEV2((sign &   1) != 0 ? -grid0.x : grid0.x,
-                                                   (sign &   2) != 0 ? -grid0.y : grid0.y);
-            buf_a[buf_idx + 1] = db * FLOAT_TYPEV2((sign &   4) != 0 ? -grid0.z : grid0.z,
-                                                   (sign &   8) != 0 ? -grid0.w : grid0.w);
-            buf_a[buf_idx + 2] = db * FLOAT_TYPEV2((sign &  16) != 0 ? -grid1.x : grid1.x,
-                                                   (sign &  32) != 0 ? -grid1.y : grid1.y);
-            buf_a[buf_idx + 3] = db * FLOAT_TYPEV2((sign &  64) != 0 ? -grid1.z : grid1.z,
-                                                   (sign & 128) != 0 ? -grid1.w : grid1.w);
+            const uint k_pair = row * LOAD_VEC_A / 2;
+            store_a(col, k_pair,     db * FLOAT_TYPEV2((sign &   1) != 0 ? -grid0.x : grid0.x,
+                                                        (sign &   2) != 0 ? -grid0.y : grid0.y));
+            store_a(col, k_pair + 1, db * FLOAT_TYPEV2((sign &   4) != 0 ? -grid0.z : grid0.z,
+                                                        (sign &   8) != 0 ? -grid0.w : grid0.w));
+            store_a(col, k_pair + 2, db * FLOAT_TYPEV2((sign &  16) != 0 ? -grid1.x : grid1.x,
+                                                        (sign &  32) != 0 ? -grid1.y : grid1.y));
+            store_a(col, k_pair + 3, db * FLOAT_TYPEV2((sign &  64) != 0 ? -grid1.z : grid1.z,
+                                                        (sign & 128) != 0 ? -grid1.w : grid1.w));
 #elif defined(DATA_A_IQ2_XS)
             const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row;
-            const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 2;
 
             const uint ib = idx / 32;            // 8 values per idx
             const uint ib32 = (idx % 32) / 4;    // 0..7
@@ -409,17 +421,17 @@ void load_a_to_shmem(const uint pos_a, const uint row, const uint col, const uin
             const vec4 grid0 = vec4(unpack8(grid.x));
             const vec4 grid1 = vec4(unpack8(grid.y));
 
-            buf_a[buf_idx    ] = db * FLOAT_TYPEV2((sign &   1) != 0 ? -grid0.x : grid0.x,
-                                                   (sign &   2) != 0 ? -grid0.y : grid0.y);
-            buf_a[buf_idx + 1] = db * FLOAT_TYPEV2((sign &   4) != 0 ? -grid0.z : grid0.z,
-                                                   (sign &   8) != 0 ? -grid0.w : grid0.w);
-            buf_a[buf_idx + 2] = db * FLOAT_TYPEV2((sign &  16) != 0 ? -grid1.x : grid1.x,
-                                                   (sign &  32) != 0 ? -grid1.y : grid1.y);
-            buf_a[buf_idx + 3] = db * FLOAT_TYPEV2((sign &  64) != 0 ? -grid1.z : grid1.z,
-                                                   (sign & 128) != 0 ? -grid1.w : grid1.w);
+            const uint k_pair = row * LOAD_VEC_A / 2;
+            store_a(col, k_pair,     db * FLOAT_TYPEV2((sign &   1) != 0 ? -grid0.x : grid0.x,
+                                                        (sign &   2) != 0 ? -grid0.y : grid0.y));
+            store_a(col, k_pair + 1, db * FLOAT_TYPEV2((sign &   4) != 0 ? -grid0.z : grid0.z,
+                                                        (sign &   8) != 0 ? -grid0.w : grid0.w));
+            store_a(col, k_pair + 2, db * FLOAT_TYPEV2((sign &  16) != 0 ? -grid1.x : grid1.x,
+                                                        (sign &  32) != 0 ? -grid1.y : grid1.y));
+            store_a(col, k_pair + 3, db * FLOAT_TYPEV2((sign &  64) != 0 ? -grid1.z : grid1.z,
+                                                        (sign & 128) != 0 ? -grid1.w : grid1.w));
 #elif defined(DATA_A_IQ2_S)
             const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row;
-            const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 2;
 
             const uint ib = idx / 32;  // 8 values per idx
             const uint ib8 = idx % 32; // 0..31
@@ -437,17 +449,17 @@ void load_a_to_shmem(const uint pos_a, const uint row, const uint col, const uin
             const vec4 grid0 = vec4(unpack8(grid.x));
             const vec4 grid1 = vec4(unpack8(grid.y));
 
-            buf_a[buf_idx    ] = db * FLOAT_TYPEV2((sign &   1) != 0 ? -grid0.x : grid0.x,
-                                                   (sign &   2) != 0 ? -grid0.y : grid0.y);
-            buf_a[buf_idx + 1] = db * FLOAT_TYPEV2((sign &   4) != 0 ? -grid0.z : grid0.z,
-                                                   (sign &   8) != 0 ? -grid0.w : grid0.w);
-            buf_a[buf_idx + 2] = db * FLOAT_TYPEV2((sign &  16) != 0 ? -grid1.x : grid1.x,
-                                                   (sign &  32) != 0 ? -grid1.y : grid1.y);
-            buf_a[buf_idx + 3] = db * FLOAT_TYPEV2((sign &  64) != 0 ? -grid1.z : grid1.z,
-                                                   (sign & 128) != 0 ? -grid1.w : grid1.w);
+            const uint k_pair = row * LOAD_VEC_A / 2;
+            store_a(col, k_pair,     db * FLOAT_TYPEV2((sign &   1) != 0 ? -grid0.x : grid0.x,
+                                                        (sign &   2) != 0 ? -grid0.y : grid0.y));
+            store_a(col, k_pair + 1, db * FLOAT_TYPEV2((sign &   4) != 0 ? -grid0.z : grid0.z,
+                                                        (sign &   8) != 0 ? -grid0.w : grid0.w));
+            store_a(col, k_pair + 2, db * FLOAT_TYPEV2((sign &  16) != 0 ? -grid1.x : grid1.x,
+                                                        (sign &  32) != 0 ? -grid1.y : grid1.y));
+            store_a(col, k_pair + 3, db * FLOAT_TYPEV2((sign &  64) != 0 ? -grid1.z : grid1.z,
+                                                        (sign & 128) != 0 ? -grid1.w : grid1.w));
 #elif defined(DATA_A_IQ3_XXS)
             const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row;
-            const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 2;
 
             const uint ib = idx / 64;            // 4 values per idx
             const uint iqs = idx % 64;           // 0..63
@@ -465,13 +477,13 @@ void load_a_to_shmem(const uint pos_a, const uint row, const uint col, const uin
             const uint grid = iq3xxs_grid[qs];
             const vec4 v = db * vec4(unpack8(grid));
 
-            buf_a[buf_idx    ] = FLOAT_TYPEV2((sign &   1) != 0 ? -v.x : v.x,
-                                              (sign &   2) != 0 ? -v.y : v.y);
-            buf_a[buf_idx + 1] = FLOAT_TYPEV2((sign &   4) != 0 ? -v.z : v.z,
-                                              (sign &   8) != 0 ? -v.w : v.w);
+            const uint k_pair = row * LOAD_VEC_A / 2;
+            store_a(col, k_pair,     FLOAT_TYPEV2((sign &   1) != 0 ? -v.x : v.x,
+                                                   (sign &   2) != 0 ? -v.y : v.y));
+            store_a(col, k_pair + 1, FLOAT_TYPEV2((sign &   4) != 0 ? -v.z : v.z,
+                                                   (sign &   8) != 0 ? -v.w : v.w));
 #elif defined(DATA_A_IQ3_S)
             const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row;
-            const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 2;
 
             const uint ib = idx / 64;            // 4 values per idx
             const uint iqs = idx % 64;           // 0..63
@@ -487,13 +499,13 @@ void load_a_to_shmem(const uint pos_a, const uint row, const uint col, const uin
             const uint32_t grid = iq3s_grid[qs | ((qh << (8 - (iqs % 8))) & 256)];
             const vec4 v = db * vec4(unpack8(grid));
 
-            buf_a[buf_idx    ] = FLOAT_TYPEV2((sign &   1) != 0 ? -v.x : v.x,
-                                              (sign &   2) != 0 ? -v.y : v.y);
-            buf_a[buf_idx + 1] = FLOAT_TYPEV2((sign &   4) != 0 ? -v.z : v.z,
-                                              (sign &   8) != 0 ? -v.w : v.w);
+            const uint k_pair = row * LOAD_VEC_A / 2;
+            store_a(col, k_pair,     FLOAT_TYPEV2((sign &   1) != 0 ? -v.x : v.x,
+                                                   (sign &   2) != 0 ? -v.y : v.y));
+            store_a(col, k_pair + 1, FLOAT_TYPEV2((sign &   4) != 0 ? -v.z : v.z,
+                                                   (sign &   8) != 0 ? -v.w : v.w));
 #elif defined(DATA_A_IQ4_XS)
             const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row;
-            const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 2;
 
             const uint ib = idx / 64;            // 4 values per idx
             const uint ib32 = (idx % 64) / 8;    // 0..7
@@ -507,11 +519,11 @@ void load_a_to_shmem(const uint pos_a, const uint row, const uint col, const uin
             const float d = float(data_a[ib].d);
             const vec4 v = d * float(int(sl | (sh << 4)) - 32) * vec4(kvalues_iq4nl[qs.x], kvalues_iq4nl[qs.y], kvalues_iq4nl[qs.z], kvalues_iq4nl[qs.w]);
 
-            buf_a[buf_idx    ] = FLOAT_TYPEV2(v.xy);
-            buf_a[buf_idx + 1] = FLOAT_TYPEV2(v.zw);
+            const uint k_pair = row * LOAD_VEC_A / 2;
+            store_a(col, k_pair,     FLOAT_TYPEV2(v.xy));
+            store_a(col, k_pair + 1, FLOAT_TYPEV2(v.zw));
 #elif defined(DATA_A_IQ4_NL)
             const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row;
-            const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 4;
 
             const uint ib = idx / 8;
             const uint iqs = idx & 0x07;
@@ -519,13 +531,13 @@ void load_a_to_shmem(const uint pos_a, const uint row, const uint col, const uin
             const FLOAT_TYPE d = FLOAT_TYPE(data_a_packed16[ib].d);
             const uint vui = uint(data_a_packed16[ib].qs[iqs]);
 
-            buf_a[buf_idx    ] = d * FLOAT_TYPEV2(kvalues_iq4nl[vui & 0xF],
-                                                  kvalues_iq4nl[bitfieldExtract(vui, 8, 4)]);
-            buf_a[buf_idx + 8] = d * FLOAT_TYPEV2(kvalues_iq4nl[bitfieldExtract(vui, 4, 4)],
-                                                  kvalues_iq4nl[vui >> 12]);
+            const uint k_pair = row * LOAD_VEC_A / 4;
+            store_a(col, k_pair,     d * FLOAT_TYPEV2(kvalues_iq4nl[vui & 0xF],
+                                                       kvalues_iq4nl[bitfieldExtract(vui, 8, 4)]));
+            store_a(col, k_pair + 8, d * FLOAT_TYPEV2(kvalues_iq4nl[bitfieldExtract(vui, 4, 4)],
+                                                       kvalues_iq4nl[vui >> 12]));
 #elif defined(DATA_A_MXFP4)
             const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row;
-            const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 4;
 
             const uint ib = idx / 8;
             const uint iqs = (idx & 0x07) * 2;
@@ -536,38 +548,37 @@ void load_a_to_shmem(const uint pos_a, const uint row, const uint col, const uin
 #ifdef USE_OCP_FP4
             const float d = e8m0_to_fp32(data_a[ib].e);
             const u8vec2 packed = u8vec2(vui, vui2);
-            buf_a[buf_idx    ] = FLOAT_TYPEV2(bitcastExtractfe2m1EXT(packed, 0u)) * FLOAT_TYPE(d);
-            buf_a[buf_idx + 8] = FLOAT_TYPEV2(bitcastExtractfe2m1EXT(packed, 4u)) * FLOAT_TYPE(d);
+            store_a(col, row,     FLOAT_TYPEV2(bitcastExtractfe2m1EXT(packed, 0u)) * FLOAT_TYPE(d));
+            store_a(col, row + 8, FLOAT_TYPEV2(bitcastExtractfe2m1EXT(packed, 4u)) * FLOAT_TYPE(d));
 #else
             const float d = e8m0_to_fp32(data_a[ib].e) * 0.5;
-            buf_a[buf_idx    ] = FLOAT_TYPEV2(kvalues_mxfp4[vui  & 0xF] * d,
-                                              kvalues_mxfp4[vui2 & 0xF] * d);
-            buf_a[buf_idx + 8] = FLOAT_TYPEV2(kvalues_mxfp4[vui  >>  4] * d,
-                                              kvalues_mxfp4[vui2 >>  4] * d);
+            store_a(col, row,     FLOAT_TYPEV2(kvalues_mxfp4[vui  & 0xF] * d,
+                                              kvalues_mxfp4[vui2 & 0xF] * d));
+            store_a(col, row + 8, FLOAT_TYPEV2(kvalues_mxfp4[vui  >>  4] * d,
+                                              kvalues_mxfp4[vui2 >>  4] * d));
 #endif
 #elif defined(DATA_A_NVFP4)
             const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row;
-            // lo and hi nibbles are 8 elements apart, which doesn't quite line up with
-            // how the thread mapping and buf_idx calculation works for other types.
-            const uint buf_idx = col * SHMEM_STRIDE + (row & 3) + (row & ~3) * 2;
-
             const uint ib = idx / 16u;
             const uint sub = (idx & 0xC) >> 2;
             const uint iqs = (idx & 0xF) * 2;
             const uint vui = uint(data_a[ib].qs[iqs]);
             const uint vui2 = uint(data_a[ib].qs[iqs+1]);
 
+            // lo and hi nibbles are 8 elements apart, which doesn't quite line up with
+            // how the thread mapping and buf_idx calculation works for other types.
+            const uint eff_row = (row & 3) + (row & ~3) * 2;
 #ifdef USE_OCP_FP4
             const FLOAT_TYPE d = FLOAT_TYPE(ue4m3_from_bits(data_a[ib].d[sub]));
             const u8vec2 packed = u8vec2(vui, vui2);
-            buf_a[buf_idx    ] = FLOAT_TYPEV2(bitcastExtractfe2m1EXT(packed, 0u)) * d;
-            buf_a[buf_idx + 4] = FLOAT_TYPEV2(bitcastExtractfe2m1EXT(packed, 4u)) * d;
+            store_a(col, eff_row,     FLOAT_TYPEV2(bitcastExtractfe2m1EXT(packed, 0u)) * d);
+            store_a(col, eff_row + 4, FLOAT_TYPEV2(bitcastExtractfe2m1EXT(packed, 4u)) * d);
 #else
             const float d = ue4m3_to_fp32(data_a[ib].d[sub]) * 0.5;
-            buf_a[buf_idx    ] = FLOAT_TYPEV2(kvalues_mxfp4[vui  & 0xF] * d,
-                                              kvalues_mxfp4[vui2 & 0xF] * d);
-            buf_a[buf_idx + 4] = FLOAT_TYPEV2(kvalues_mxfp4[vui  >>  4] * d,
-                                              kvalues_mxfp4[vui2 >>  4] * d);
+            store_a(col, eff_row,     FLOAT_TYPEV2(kvalues_mxfp4[vui  & 0xF] * d,
+                                                    kvalues_mxfp4[vui2 & 0xF] * d));
+            store_a(col, eff_row + 4, FLOAT_TYPEV2(kvalues_mxfp4[vui  >>  4] * d,
+                                                    kvalues_mxfp4[vui2 >>  4] * d));
 #endif
 #endif
 }

gguf-py/gguf/constants.py

@@ -124,6 +124,7 @@ class Keys:
         EXPERT_WEIGHTS_NORM               = "{arch}.expert_weights_norm"
         EXPERT_GATING_FUNC                = "{arch}.expert_gating_func"
         EXPERT_GROUP_SCALE                = "{arch}.expert_group_scale"
+        EXPERT_LATENT_LENGTH              = "{arch}.expert_latent_length"
         EXPERTS_PER_GROUP                 = "{arch}.experts_per_group"
         MOE_EVERY_N_LAYERS                = "{arch}.moe_every_n_layers"
         MOE_LATENT_SIZE                   = "{arch}.moe_latent_size"
@@ -238,6 +239,13 @@ class Keys:
         SCALING_YARN_BETA_FAST    = "{arch}.rope.scaling.yarn_beta_fast"
         SCALING_YARN_BETA_SLOW    = "{arch}.rope.scaling.yarn_beta_slow"
 
+    class Activation:
+        SITU_BETA        = "{arch}.activation.situ_beta"
+        SITU_LINEAR_BETA = "{arch}.activation.situ_linear_beta"
+
+    class AttnRes:
+        BLOCK_SIZE = "{arch}.attn_res.block_size"
+
     class Split:
         LLM_KV_SPLIT_NO            = "split.no"
         LLM_KV_SPLIT_COUNT         = "split.count"
@@ -252,7 +260,8 @@ class Keys:
         DT_B_C_RMS     = "{arch}.ssm.dt_b_c_rms"
 
     class KDA:
-        HEAD_DIM = "{arch}.kda.head_dim"
+        HEAD_DIM         = "{arch}.kda.head_dim"
+        GATE_LOWER_BOUND = "{arch}.kda.gate_lower_bound"
 
     class WKV:
         HEAD_SIZE = "{arch}.wkv.head_size"
@@ -565,6 +574,7 @@ class MODEL_ARCH(IntEnum):
     GROVEMOE         = auto()
     APERTUS          = auto()
     COGVLM           = auto()
+    MINIMAX01        = auto()
     MINIMAXM2        = auto()
     MINIMAXM3        = auto()
     RND1             = auto()
@@ -579,6 +589,7 @@ class MODEL_ARCH(IntEnum):
     LLAMA_EMBED      = auto()
     MAINCODER        = auto()
     KIMI_LINEAR      = auto()
+    KIMI_K3          = auto()
     TALKIE           = auto()
     MELLUM           = auto()
     NANBEIGE         = auto()
@@ -697,6 +708,13 @@ class MODEL_TENSOR(IntEnum):
     SSM_BETA             = auto() # Kimi Linear qwen3.5
     SSM_G_A              = auto() # Kimi Linear
     SSM_G_B              = auto() # Kimi Linear
+    SSM_G                = auto() # Kimi K3 (full-rank KDA gate, replaces SSM_G_A/SSM_G_B)
+    ATTN_RES_SCORE       = auto() # Kimi K3 (fused res_norm * res_proj, pre-attention)
+    FFN_RES_SCORE        = auto() # Kimi K3 (fused res_norm * res_proj, pre-FFN)
+    OUTPUT_RES_SCORE     = auto() # Kimi K3 (fused res_norm * res_proj, final)
+    FFN_ROUTED_DOWN      = auto() # Kimi K3 (latent MoE: hidden -> latent)
+    FFN_ROUTED_UP        = auto() # Kimi K3 (latent MoE: latent -> hidden)
+    FFN_ROUTED_NORM      = auto() # Kimi K3 (latent MoE: norm on expert output)
     TIME_MIX_W0          = auto()
     TIME_MIX_W1          = auto()
     TIME_MIX_W2          = auto()
@@ -1271,6 +1289,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
     MODEL_ARCH.SEED_OSS:         "seed_oss",
     MODEL_ARCH.GROVEMOE:         "grovemoe",
     MODEL_ARCH.APERTUS:          "apertus",
+    MODEL_ARCH.MINIMAX01:        "minimax-01",
     MODEL_ARCH.MINIMAXM2:        "minimax-m2",
     MODEL_ARCH.MINIMAXM3:        "minimax-m3",
     MODEL_ARCH.COGVLM:           "cogvlm",
@@ -1286,6 +1305,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
     MODEL_ARCH.LLAMA_EMBED:      "llama-embed",
     MODEL_ARCH.MAINCODER:        "maincoder",
     MODEL_ARCH.KIMI_LINEAR:      "kimi-linear",
+    MODEL_ARCH.KIMI_K3:          "kimi-k3",
     MODEL_ARCH.TALKIE:           "talkie",
     MODEL_ARCH.MELLUM:           "mellum",
     MODEL_ARCH.NANBEIGE:         "nanbeige",
@@ -1402,6 +1422,13 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
     MODEL_TENSOR.SSM_BETA:                  "blk.{bid}.ssm_beta",             # Kimi Linear qwen3.5
     MODEL_TENSOR.SSM_G_A:                   "blk.{bid}.ssm_g_a",              # Kimi Linear
     MODEL_TENSOR.SSM_G_B:                   "blk.{bid}.ssm_g_b",              # Kimi Linear
+    MODEL_TENSOR.SSM_G:                     "blk.{bid}.ssm_g",                # Kimi K3
+    MODEL_TENSOR.ATTN_RES_SCORE:            "blk.{bid}.attn_res_score",       # Kimi K3
+    MODEL_TENSOR.FFN_RES_SCORE:             "blk.{bid}.ffn_res_score",        # Kimi K3
+    MODEL_TENSOR.OUTPUT_RES_SCORE:          "output_res_score",               # Kimi K3
+    MODEL_TENSOR.FFN_ROUTED_DOWN:           "blk.{bid}.ffn_routed_down",      # Kimi K3
+    MODEL_TENSOR.FFN_ROUTED_UP:             "blk.{bid}.ffn_routed_up",        # Kimi K3
+    MODEL_TENSOR.FFN_ROUTED_NORM:           "blk.{bid}.ffn_routed_norm",      # Kimi K3
     MODEL_TENSOR.TIME_MIX_W0:               "blk.{bid}.time_mix_w0",
     MODEL_TENSOR.TIME_MIX_W1:               "blk.{bid}.time_mix_w1",
     MODEL_TENSOR.TIME_MIX_W2:               "blk.{bid}.time_mix_w2",
@@ -4592,6 +4619,24 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_DOWN_CHEXP,
         MODEL_TENSOR.FFN_UP_CHEXP,
     ],
+    MODEL_ARCH.MINIMAX01: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_NORM_2,
+        MODEL_TENSOR.ATTN_QKV,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.ATTN_GATE,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_GATE_INP,
+        MODEL_TENSOR.FFN_GATE_EXP,
+        MODEL_TENSOR.FFN_DOWN_EXP,
+        MODEL_TENSOR.FFN_UP_EXP,
+    ],
     MODEL_ARCH.MINIMAXM2: [
         MODEL_TENSOR.TOKEN_EMBD,
         MODEL_TENSOR.OUTPUT_NORM,
@@ -4940,6 +4985,56 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_DOWN_SHEXP,
         MODEL_TENSOR.FFN_UP_SHEXP,
     ],
+    MODEL_ARCH.KIMI_K3: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.OUTPUT_RES_SCORE,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_RES_SCORE,
+        MODEL_TENSOR.FFN_RES_SCORE,
+        # MLA (full-attention layers)
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.ATTN_GATE,
+        MODEL_TENSOR.ATTN_Q_A,
+        MODEL_TENSOR.ATTN_Q_B,
+        MODEL_TENSOR.ATTN_KV_A_MQA,
+        MODEL_TENSOR.ATTN_KV_B,
+        MODEL_TENSOR.ATTN_K_B,
+        MODEL_TENSOR.ATTN_V_B,
+        MODEL_TENSOR.ATTN_Q_A_NORM,
+        MODEL_TENSOR.ATTN_KV_A_NORM,
+        # KDA (linear-attention layers)
+        MODEL_TENSOR.SSM_CONV1D_Q,
+        MODEL_TENSOR.SSM_CONV1D_K,
+        MODEL_TENSOR.SSM_CONV1D_V,
+        MODEL_TENSOR.SSM_F_A,
+        MODEL_TENSOR.SSM_F_B,
+        MODEL_TENSOR.SSM_BETA,
+        MODEL_TENSOR.SSM_A,
+        MODEL_TENSOR.SSM_G,
+        MODEL_TENSOR.SSM_DT,
+        MODEL_TENSOR.SSM_NORM,
+        # FFN
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+        MODEL_TENSOR.FFN_GATE_INP,
+        MODEL_TENSOR.FFN_EXP_PROBS_B,
+        MODEL_TENSOR.FFN_GATE_EXP,
+        MODEL_TENSOR.FFN_DOWN_EXP,
+        MODEL_TENSOR.FFN_UP_EXP,
+        MODEL_TENSOR.FFN_GATE_SHEXP,
+        MODEL_TENSOR.FFN_DOWN_SHEXP,
+        MODEL_TENSOR.FFN_UP_SHEXP,
+        MODEL_TENSOR.FFN_ROUTED_DOWN,
+        MODEL_TENSOR.FFN_ROUTED_UP,
+        MODEL_TENSOR.FFN_ROUTED_NORM,
+    ],
     MODEL_ARCH.TALKIE: [
         MODEL_TENSOR.TOKEN_EMBD,
         MODEL_TENSOR.OUTPUT,

gguf-py/gguf/gguf_writer.py

@@ -1103,6 +1103,21 @@ class GGUFWriter:
     def add_ssm_dt_b_c_rms(self, value: bool) -> None:
         self.add_bool(Keys.SSM.DT_B_C_RMS.format(arch=self.arch), value)
 
+    def add_kda_gate_lower_bound(self, value: float) -> None:
+        self.add_float32(Keys.KDA.GATE_LOWER_BOUND.format(arch=self.arch), value)
+
+    def add_expert_latent_length(self, value: int) -> None:
+        self.add_uint32(Keys.LLM.EXPERT_LATENT_LENGTH.format(arch=self.arch), value)
+
+    def add_activation_situ_beta(self, value: float) -> None:
+        self.add_float32(Keys.Activation.SITU_BETA.format(arch=self.arch), value)
+
+    def add_activation_situ_linear_beta(self, value: float) -> None:
+        self.add_float32(Keys.Activation.SITU_LINEAR_BETA.format(arch=self.arch), value)
+
+    def add_attn_res_block_size(self, value: int) -> None:
+        self.add_uint32(Keys.AttnRes.BLOCK_SIZE.format(arch=self.arch), value)
+
     def add_kda_head_dim(self, value: int) -> None:
         self.add_uint32(Keys.KDA.HEAD_DIM.format(arch=self.arch), value)
 

gguf-py/gguf/tensor_mapping.py

@@ -225,6 +225,7 @@ class TensorNameMap:
             "rwkv.blocks.{bid}.ln2",                        # rwkv6
             "model.layers.{bid}.ln2",                       # rwkv7
             "model.layers.{bid}.post_attention_layernorm",  # cogvlm
+            "model.layers.{bid}.self_attn.norm",            # minimax-01
         ),
 
         # Attention query-key-value
@@ -321,7 +322,7 @@ class TensorNameMap:
             "h.{bid}.self_attention.dense",                                 # bloom
             "model.layers.{bid}.self_attn.o_proj",                          # llama-hf nemotron olmoe olmo2 phimoe
             "layers.{bid}.self_attn.o_proj",                                # embeddinggemma
-            "model.layers.{bid}.self_attn.out_proj",                        # lfm2
+            "model.layers.{bid}.self_attn.out_proj",                        # lfm2 minimax-01
             "model.layers.{bid}.self_attn.linear_attn",                     # deci
             "layers.{bid}.attention.wo",                                    # llama-pth
             "encoder.layer.{bid}.attention.output.dense",                   # bert
@@ -385,6 +386,7 @@ class TensorNameMap:
             "model.layers.{bid}.self_attn.gate_proj", # afmoe muse-glimmer
             "model.layers.{bid}.linear_attn.in_proj_z",  # qwen3.5
             "model.layers.{bid}.self_attn.g_proj",    # step3.5 head-wise attention gate
+            "model.layers.{bid}.self_attn.output_gate",  # minimax-01
         ),
 
         # Feed-forward norm
@@ -910,6 +912,19 @@ class TensorNameMap:
             "model.layers.{bid}.linear_attn.in_proj_b",  # qwen3.5
             "model.layers.{bid}.self_attn.b_proj",       # Kimi Linear
         ),
+        # Kimi K3 latent MoE: routed experts operate in a down-projected space
+        MODEL_TENSOR.FFN_ROUTED_DOWN: (
+            "model.layers.{bid}.block_sparse_moe.routed_expert_down_proj",
+        ),
+
+        MODEL_TENSOR.FFN_ROUTED_UP: (
+            "model.layers.{bid}.block_sparse_moe.routed_expert_up_proj",
+        ),
+
+        MODEL_TENSOR.FFN_ROUTED_NORM: (
+            "model.layers.{bid}.block_sparse_moe.routed_expert_norm",
+        ),
+
         MODEL_TENSOR.SSM_G_A: (
             "model.layers.{bid}.self_attn.g_a_proj",
         ),

models/templates/Kimi-K3.jinja

@@ -0,0 +1,324 @@
+{%- macro escape_attr(value) -%}
+{{- value|string|replace('&', '&')|replace('"', '"') -}}
+{%- endmacro -%}
+
+{%- macro open_tag(tag, attrs=[]) -%}
+{{- '<|open|>' + tag -}}
+{%- for attr in attrs -%}
+{{- ' ' + attr[0] + '="' -}}{{- escape_attr(attr[1]) -}}{{- '"' -}}
+{%- endfor -%}
+{{- '<|sep|>' -}}
+{%- endmacro -%}
+
+{%- macro close_tag(tag) -%}
+{{- '<|close|>' + tag + '<|sep|>' -}}
+{%- endmacro -%}
+
+{%- macro next_image(state) -%}
+{%- if image_prompts is defined and image_prompts is not none -%}
+    {%- if state.image_index >= image_prompts|length -%}
+        {{- raise_exception('More image placeholders than image prompts.') -}}
+    {%- endif -%}
+    {{- image_prompts[state.image_index] -}}
+    {%- set state.image_index = state.image_index + 1 -%}
+{%- else -%}
+    {{- '<|kimi_image_placeholder|>' -}}
+{%- endif -%}
+{%- endmacro -%}
+
+{%- macro render_text(text, state) -%}
+{%- set text = text|string -%}
+{%- if image_prompts is defined and image_prompts is not none and '<|kimi_image_placeholder|>' in text -%}
+    {%- set parts = text.split('<|kimi_image_placeholder|>') -%}
+    {%- for part in parts -%}
+        {{- part -}}
+        {%- if not loop.last -%}{{- next_image(state) -}}{%- endif -%}
+    {%- endfor -%}
+{%- else -%}
+    {{- text -}}
+{%- endif -%}
+{%- endmacro -%}
+
+{%- macro render_content(content, state) -%}
+{%- if content is string -%}
+    {{- render_text(content, state) -}}
+{%- elif content is not none and content is defined -%}
+    {%- for part in content -%}
+        {%- if part.type in ['image', 'image_url'] -%}
+            {{- next_image(state) -}}
+        {%- else -%}
+            {{- render_text(part.text, state) -}}
+        {%- endif -%}
+    {%- endfor -%}
+{%- endif -%}
+{%- endmacro -%}
+
+{%- macro internal_system_message(message_type, body) -%}
+{{- open_tag('message', [('role', 'system'), ('type', message_type)]) -}}
+{{- body|trim -}}
+{{- close_tag('message') -}}
+{{- '<|end_of_msg|>' -}}
+{%- endmacro -%}
+
+{%- macro json_sorted(value) -%}
+{#- tojson has no sort_keys, so sort each mapping level with dictsort to match the
+    reference implementation. Array order is kept as-is. -#}
+{%- if value is mapping -%}
+{{- '{' -}}
+{%- for key, item in value|dictsort -%}
+{%- if not loop.first -%}{{- ',' -}}{%- endif -%}
+{{- key|tojson(ensure_ascii=false) -}}{{- ':' -}}{{- json_sorted(item) -}}
+{%- endfor -%}
+{{- '}' -}}
+{%- elif value is string or value is number or value is boolean or value is none -%}
+{{- value|tojson(ensure_ascii=false) -}}
+{%- else -%}
+{{- '[' -}}
+{%- for item in value -%}
+{%- if not loop.first -%}{{- ',' -}}{%- endif -%}
+{{- json_sorted(item) -}}
+{%- endfor -%}
+{{- ']' -}}
+{%- endif -%}
+{%- endmacro -%}
+
+{%- macro render_tool_declare(tool_list, dynamic=false) -%}
+{{- open_tag('message', [('role', 'system'), ('type', 'tool-declare')]) -}}
+{%- if dynamic -%}
+{{- '## New Tools Available\nThe system dynamically extends the toolset via lazy-loading.\nYou have access to all existing and extended tools.\nHere are the specs for the extended tools.\n\n```json\n' -}}
+{%- else -%}
+{{- '# Tools\nHere are the available tools, described in JSONSchema.\n\n```json\n' -}}
+{%- endif -%}
+{{- json_sorted(tool_list) -}}
+{{- '\n```' -}}
+{{- close_tag('message') -}}
+{{- '<|end_of_msg|>' -}}
+{%- endmacro -%}
+
+{%- macro xtml_type(value) -%}
+{%- if value is boolean -%}boolean
+{%- elif value is none -%}null
+{%- elif value is number -%}number
+{%- elif value is string -%}string
+{%- elif value is mapping -%}object
+{%- else -%}array
+{%- endif -%}
+{%- endmacro -%}
+
+{%- macro xtml_value(value) -%}
+{%- if value is string -%}
+{{- value -}}
+{%- else -%}
+{{- value|tojson(ensure_ascii=false) -}}
+{%- endif -%}
+{%- endmacro -%}
+
+{%- macro render_assistant(message, state) -%}
+{%- if thinking -%}
+    {%- set reasoning_content = message.get('reasoning_content') or message.get('reasoning') -%}
+    {{- open_tag('think') -}}
+    {%- if reasoning_content is not none and reasoning_content|string|trim -%}
+        {{- render_text(reasoning_content, state) -}}
+    {%- endif -%}
+    {{- close_tag('think') -}}
+{%- endif -%}
+{{- open_tag('response') -}}
+{{- render_content(message.get('content'), state) -}}
+{{- close_tag('response') -}}
+{%- set tool_calls = message.get('tool_calls') -%}
+{%- if tool_calls -%}
+    {{- open_tag('tools') -}}
+    {%- for tool_call in tool_calls -%}
+        {%- if tool_call is not mapping -%}
+            {{- raise_exception('Kimi K3 tool calls must be mappings.') -}}
+        {%- endif -%}
+        {%- set fn = tool_call.function if tool_call.function is defined and tool_call.function is mapping else tool_call -%}
+        {%- if fn.get('name') is none -%}
+            {{- raise_exception('Kimi K3 tool calls require a function name.') -}}
+        {%- endif -%}
+        {{- open_tag('call', [('tool', fn.name), ('index', loop.index)]) -}}
+        {%- set arguments = fn.get('arguments', {}) -%}
+        {%- set json_block = fn.get('_xtml_json_block') -%}
+        {%- if json_block is not none -%}
+            {{- open_tag('json', [('type', 'object')]) -}}
+            {{- render_text(json_block, state) -}}
+            {{- close_tag('json') -}}
+        {%- elif arguments is mapping -%}
+            {%- for key, value in arguments.items() -%}
+                {{- open_tag('argument', [('key', key), ('type', xtml_type(value))]) -}}
+                {{- render_text(xtml_value(value), state) -}}
+                {{- close_tag('argument') -}}
+            {%- endfor -%}
+        {%- elif arguments is string and arguments|trim -%}
+            {{- open_tag('json', [('type', 'object')]) -}}
+            {{- render_text(arguments, state) -}}
+            {{- close_tag('json') -}}
+        {%- elif arguments is not none and arguments is not string -%}
+            {{- raise_exception('Kimi K3 tool call arguments must be a mapping or a JSON object string.') -}}
+        {%- endif -%}
+        {{- close_tag('call') -}}
+    {%- endfor -%}
+    {{- close_tag('tools') -}}
+{%- endif -%}
+{%- endmacro -%}
+
+{%- macro render_tool_message(message, state, resolved_name=none) -%}
+{%- set state.tool_index = state.tool_index + 1 -%}
+{%- if resolved_name is not none -%}
+    {%- set tool_name = resolved_name -%}
+{%- elif 'tool' in message -%}
+    {%- set tool_name = message.get('tool') -%}
+{%- else -%}
+    {%- set tool_name = message.get('name') -%}
+{%- endif -%}
+{%- if tool_name is none and state.tool_calls is not none and state.tool_index <= state.tool_calls|length -%}
+    {%- set fallback_call = state.tool_calls[state.tool_index - 1] -%}
+    {%- set fallback_fn = fallback_call.function if fallback_call.function is defined and fallback_call.function is mapping else fallback_call -%}
+    {%- set tool_name = fallback_fn.name -%}
+{%- endif -%}
+{%- if tool_name is none -%}
+    {{- raise_exception('Kimi K3 tool messages need a resolvable tool name: carry `tool`/`name`, or match a preceding assistant tool_call by order.') -}}
+{%- endif -%}
+{{- open_tag('message', [('role', 'tool'), ('tool', tool_name), ('index', state.tool_index)]) -}}
+{{- render_content(message.get('content'), state) -}}
+{{- close_tag('message') -}}
+{{- '<|end_of_msg|>' -}}
+{%- endmacro -%}
+
+{%- if thinking is undefined -%}
+    {%- set thinking = true -%}
+{%- endif -%}
+{%- if thinking_effort is undefined -%}
+    {%- set thinking_effort = 'max' -%}
+{%- endif -%}
+{%- if thinking and thinking_effort is not none and thinking_effort not in ['low', 'high', 'max'] -%}
+    {{- raise_exception('Unsupported thinking_effort=' + thinking_effort|string + '; supported values are low, high, and max.') -}}
+{%- endif -%}
+
+{%- set state = namespace(image_index=0, tool_calls=none, tool_index=0, response_schema=none) -%}
+
+{%- if tools is defined and tools -%}
+    {{- render_tool_declare(tools) -}}
+{%- endif -%}
+
+{%- if thinking and thinking_effort in ['low', 'high', 'max'] -%}
+    {{- internal_system_message(
+        'thinking-effort',
+        '`thinking_effort` guides on how much to think in your thinking channel (not including the response channel), supported values include `low`, `medium`, `high`, and `max`.\nNow the system is invoked with `thinking_effort=' + thinking_effort|string + '`.'
+    ) -}}
+{%- endif -%}
+
+{%- for message in messages -%}
+    {%- if message is mapping -%}
+        {%- if 'role' not in message -%}
+            {{- raise_exception('Kimi K3 messages require a role.') -}}
+        {%- elif message.role == 'user' -%}
+            {%- set attrs = [('role', 'user')] -%}
+            {%- if message.get('name') -%}{%- set attrs = attrs + [('name', message.name)] -%}{%- endif -%}
+            {{- open_tag('message', attrs) -}}
+            {{- render_content(message.get('content'), state) -}}
+            {{- close_tag('message') -}}
+            {{- '<|end_of_msg|>' -}}
+        {%- elif message.role == 'system' and message.get('tools') -%}
+            {{- render_tool_declare(message.tools, dynamic=true) -}}
+        {%- elif message.role == 'system' -%}
+            {%- set attrs = [('role', 'system')] -%}
+            {%- if message.get('name') -%}{%- set attrs = attrs + [('name', message.name)] -%}{%- endif -%}
+            {{- open_tag('message', attrs) -}}
+            {{- render_content(message.get('content'), state) -}}
+            {{- close_tag('message') -}}
+            {{- '<|end_of_msg|>' -}}
+        {%- elif message.role == 'assistant' -%}
+            {%- set state.tool_calls = message.get('tool_calls') -%}
+            {%- set state.tool_index = 0 -%}
+            {%- set attrs = [('role', 'assistant')] -%}
+            {%- if message.get('name') -%}{%- set attrs = attrs + [('name', message.name)] -%}{%- endif -%}
+            {{- open_tag('message', attrs) -}}
+            {{- render_assistant(message, state) -}}
+            {{- close_tag('message') -}}
+            {{- '<|end_of_msg|>' -}}
+        {%- elif message.role == 'tool' and (loop.first or messages[loop.index0 - 1].role != 'tool') -%}
+            {%- set run = namespace(tool_messages=[], resolved_count=0) -%}
+            {%- for candidate in messages[loop.index0:] -%}
+                {%- if candidate is not mapping or candidate.role != 'tool' -%}{%- break -%}{%- endif -%}
+                {%- set run.tool_messages = run.tool_messages + [candidate] -%}
+                {%- set call_id = candidate.get('tool_call_id', candidate.get('id')) -%}
+                {%- set match = namespace(found=false) -%}
+                {%- if call_id is not none and state.tool_calls is not none -%}
+                    {%- for tool_call in state.tool_calls -%}
+                        {%- if not match.found and tool_call is mapping and tool_call.get('id') is not none and tool_call.get('id')|string == call_id|string -%}
+                            {%- set match.found = true -%}
+                        {%- endif -%}
+                    {%- endfor -%}
+                {%- endif -%}
+                {%- if match.found -%}{%- set run.resolved_count = run.resolved_count + 1 -%}{%- endif -%}
+            {%- endfor -%}
+            {%- if run.tool_messages|length > 0 and run.resolved_count == run.tool_messages|length -%}
+                {%- set emitted = namespace(ids=[]) -%}
+                {%- for tool_call in state.tool_calls -%}
+                    {%- if tool_call is mapping and tool_call.get('id') is not none and tool_call.get('id')|string not in emitted.ids -%}
+                        {%- set emitted.ids = emitted.ids + [tool_call.get('id')|string] -%}
+                        {%- set fn = tool_call.function if tool_call.function is defined and tool_call.function is mapping else tool_call -%}
+                        {%- for tool_message in run.tool_messages -%}
+                            {%- set result_id = tool_message.get('tool_call_id', tool_message.get('id')) -%}
+                            {%- if result_id is not none and result_id|string == tool_call.get('id')|string -%}
+                                {{- render_tool_message(tool_message, state, fn.get('name')) -}}
+                            {%- endif -%}
+                        {%- endfor -%}
+                    {%- endif -%}
+                {%- endfor -%}
+            {%- else -%}
+                {%- for tool_message in run.tool_messages -%}
+                    {{- render_tool_message(tool_message, state) -}}
+                {%- endfor -%}
+            {%- endif -%}
+        {%- endif -%}
+    {%- endif -%}
+{%- endfor -%}
+
+{%- if tool_choice is defined and tool_choice == 'required' -%}
+    {{- internal_system_message('tool-choice', 'The system is invoked with `tool_choice=required`.\nYou MUST call tools in the next message.') -}}
+{%- elif tool_choice is defined and tool_choice == 'none' -%}
+    {{- internal_system_message('tool-choice', 'The system is invoked with `tool_choice=none`.\nYou MUST NOT call any tools in the next message.') -}}
+{%- endif -%}
+
+{%- if response_schema is defined -%}
+    {%- set state.response_schema = response_schema -%}
+{%- elif response_format is defined and response_format is mapping and response_format.get('json_schema') is not none -%}
+    {%- set schema_wrapper = response_format.get('json_schema') -%}
+    {%- if schema_wrapper is mapping and 'schema' in schema_wrapper -%}
+        {%- set state.response_schema = schema_wrapper.get('schema') -%}
+    {%- elif schema_wrapper is mapping and 'json_schema' in schema_wrapper -%}
+        {%- set state.response_schema = schema_wrapper.get('json_schema') -%}
+    {%- else -%}
+        {%- set state.response_schema = schema_wrapper -%}
+    {%- endif -%}
+{%- endif -%}
+
+{%- set response_format_type = none -%}
+{%- if response_format is defined and response_format is mapping -%}
+    {%- set response_format_type = response_format.get('type') -%}
+{%- elif response_format is defined -%}
+    {%- set response_format_type = response_format -%}
+{%- endif -%}
+{%- if response_format_type == 'json_object' -%}
+    {{- internal_system_message(
+        'response-format',
+        'The system is invoked with `response_format=json_object`.\nYour response must be raw JSON data without markdown code blocks (```json) or any additional formatting.'
+    ) -}}
+{%- elif response_format_type == 'json_schema' -%}
+    {{- internal_system_message(
+        'response-format',
+        'The system is invoked with `response_format=json_schema`.\nYour response must be raw JSON data without markdown code blocks (```json) or any additional formatting.\nThe JSON data must match the following schema:\n```json\n' + json_sorted(state.response_schema) + '\n```'
+    ) -}}
+{%- endif -%}
+
+{%- if add_generation_prompt -%}
+    {{- open_tag('message', [('role', 'assistant')]) -}}
+    {{- open_tag('think' if thinking else 'response') -}}
+{%- endif -%}
+
+{%- if image_prompts is defined and image_prompts is not none and state.image_index != image_prompts|length -%}
+    {{- raise_exception('image prompt count ' + image_prompts|length|string + ' != consumed placeholder count ' + state.image_index|string) -}}
+{%- endif -%}
+

models/templates/MiniMax-M1.jinja

@@ -0,0 +1,91 @@
+{{ '<begin_of_document>' -}}
+{%- if custom_tools is defined %}
+    {%- set tools = custom_tools %}
+{%- endif %}
+{%- if not tools is defined %}
+    {%- set tools = none %}
+{%- endif %}
+
+{#- Extract system message #}
+{% set ns = namespace(system_prompt='') -%}
+{%- if messages[0]['role'] == 'system' %}
+    {%- if messages[0]['content'] is string %}
+        {%- set ns.system_prompt = messages[0]['content']|trim %}
+    {%- else %}
+        {%- set ns.system_prompt = messages[0]['content'][0]['text']|trim %}
+    {%- endif %}
+    {%- set messages = messages[1:] %}
+{%- else %}
+    {%- if tools is not none %}
+        {%- set ns.system_prompt = "You are a helpful assistant created by Minimax based on MiniMax-M1 model." %}
+    {%- else %}
+        {%- set ns.system_prompt = "You are a helpful assistant created by Minimax based on MiniMax-M1 model." %}
+    {%- endif %}
+{%- endif %}
+
+{#- System message #}
+{%- if ns.system_prompt != '' %}
+{{ '<beginning_of_sentence>system ai_setting=assistant\n' + ns.system_prompt + '<end_of_sentence>\n' -}}
+{%- endif %}
+
+{#- Tools configuration #}
+{%- if tools is not none %}
+{{ '<beginning_of_sentence>system tool_setting=tools\nYou are provided with these tools:\n<tools>\n' -}}
+{%- for tool in tools %}
+{{ tool | tojson ~ '\n' -}}
+{%- endfor %}
+{{ '</tools>\n\nIf you need to call tools, please respond with <tool_calls></tool_calls> XML tags, and provide tool-name and json-object of arguments, following the format below:\n<tool_calls>\n{"name": <tool-name>, "arguments": <args-json-object>}\n...\n</tool_calls><end_of_sentence>\n' -}}
+{%- endif %}
+
+{#- Process messages #}
+{%- for message in messages %}
+    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
+        {%- if message['role'] == 'user' %}
+{{ '<beginning_of_sentence>user name=user\n' -}}
+{%- if message['content'] is string %}
+{{ message['content']|trim -}}
+{%- else %}
+{%- for content in message['content'] %}
+{%- if content['type'] == 'text' %}
+{{ content['text']|trim -}}
+{%- endif %}
+{%- endfor %}
+{%- endif %}
+{{ '<end_of_sentence>\n' -}}
+        {%- elif message['role'] == 'assistant' %}
+{{ '<beginning_of_sentence>ai name=assistant\n' -}}
+{%- if message['content'] is string %}
+{{ message['content']|trim -}}
+{%- else %}
+{%- for content in message['content'] | selectattr('type', 'equalto', 'text') %}
+{{ content['text']|trim -}}
+{%- endfor %}
+{%- endif %}
+{{ '<end_of_sentence>\n' -}}
+        {%- endif %}
+    {%- elif 'tool_calls' in message %}
+{{ '<beginning_of_sentence>ai name=assistant\n<tool_calls>\n' -}}
+{%- for tool_call in message.tool_calls %}
+{{ '{"name": "' + tool_call.function.name + '", "arguments": ' + tool_call.function.arguments | tojson + '}\n' -}}
+{%- endfor %}
+{{ '</tool_calls><end_of_sentence>\n' -}}
+    {%- elif message.role == "tool" or message.role == "ipython" %}
+{{ '<beginning_of_sentence>tool name=tools\n' -}}
+{%- if message.content is string %}
+{{ 'tool result: ' + message.content + '\n\n' -}}
+{%- else %}
+{%- for content in message['content'] %}
+{%- if content['type'] == 'text' %}
+{{ 'tool result: ' + content['text'] + '\n\n' -}}
+{%- elif content.get('name') %}
+{{ 'tool name: ' + content['name'] + '\ntool result: ' + content['text'] + '\n\n' -}}
+{%- endif %}
+{%- endfor %}
+{%- endif %}
+{{ '<end_of_sentence>\n' -}}
+    {%- endif %}
+{%- endfor %}
+
+{%- if add_generation_prompt %}
+{{ '<beginning_of_sentence>ai name=assistant\n' -}}
+{%- endif %}

scripts/bench-models.sh

@@ -22,8 +22,8 @@ if (( QUICK )); then
 fi
 
 if (( DIO )); then
-    ARGS_BB="${ARGS_BB} --no-mmap --direct-io"
-    ARGS_B="${ARGS_B} -mmp 0 -dio 1"
+    ARGS_BB="${ARGS_BB} --load-mode dio"
+    ARGS_B="${ARGS_B} --load-mode dio"
 fi
 
 run_model() {

scripts/snapdragon/adb/run-bench.sh

@@ -43,7 +43,7 @@ adb $adbserial $adbhost shell " \
   cd $basedir;         \
   LD_LIBRARY_PATH=$basedir/$branch/lib   \
   ADSP_LIBRARY_PATH=$basedir/$branch/lib \
-    $ndev $nhvx $opmask $verbose $profile $hb ./$branch/bin/llama-bench --device $device --mmap 0 -m $basedir/../gguf/$model \
+    $ndev $nhvx $opmask $verbose $profile $hb ./$branch/bin/llama-bench --device $device --load-mode none -m $basedir/../gguf/$model \
         --poll 1000 -t 6 --cpu-mask 0xfc --cpu-strict 1 \
         --ubatch-size 1024 -fa 1 -ngl 99 $cli_opts $@   \
 "

scripts/snapdragon/adb/run-cli.sh

@@ -71,7 +71,7 @@ adb $adbserial $adbhost shell " \
     LD_LIBRARY_PATH=$basedir/$branch/lib   \
     ADSP_LIBRARY_PATH=$basedir/$branch/lib \
     $verbose $sched $opmask $profile $nhvx $hmx $ndev $hb $opbatch $opqueue $opflt $vmem $mbuf \
-      ./$branch/bin/llama-cli --no-mmap -m $basedir/../gguf/$model \
+      ./$branch/bin/llama-cli --load-mode none -m $basedir/../gguf/$model \
          --poll 1000 -t 6 --cpu-mask 0xfc --cpu-strict 1           \
          --ctx-size 8192 --ubatch-size 1024 -fa on                 \
          -ngl 99 --device $device $cli_opts $@                     \

scripts/snapdragon/adb/run-completion.sh

@@ -79,7 +79,7 @@ adb $adbserial $adbhost shell " \
     LD_LIBRARY_PATH=$basedir/$branch/lib   \
     ADSP_LIBRARY_PATH=$basedir/$branch/lib \
     $verbose $sched $opmask $profile $nhvx $hmx $ndev $hb $opbatch $opqueue $oppoll $opflt $opfuse $vmem $mbuf $mmsel $fasel \
-      ./$branch/bin/llama-completion --no-mmap -m $basedir/../gguf/$model \
+      ./$branch/bin/llama-completion --load-mode none -m $basedir/../gguf/$model \
          --poll 1000 -t 6 --cpu-mask 0xfc --cpu-strict 1                  \
          --ctx-size 8192 --ubatch-size 1024 -fa on                        \
          -ngl 99 --device $device $cli_opts $@                            \

scripts/snapdragon/adb/run-mtmd.sh

@@ -62,7 +62,7 @@ adb $adbserial $adbhost shell " \
     LD_LIBRARY_PATH=$basedir/$branch/lib   \
     ADSP_LIBRARY_PATH=$basedir/$branch/lib \
     $verbose $experimental $sched $opmask $profile $hmx $nhvx $ndev $mtmd_backend \
-      ./$branch/bin/llama-mtmd-cli --no-mmap -m $basedir/../gguf/$model      \
+      ./$branch/bin/llama-mtmd-cli --load-mode none -m $basedir/../gguf/$model      \
          --mmproj $basedir/../gguf/$mmproj                                   \
          --image $basedir/../gguf/$image                                     \
          --poll 1000 -t 6 --cpu-mask 0xfc --cpu-strict 1                     \

scripts/snapdragon/windows/run-bench.ps1

@@ -43,6 +43,6 @@ if ($null -ne $env:HB) {
 $env:ADSP_LIBRARY_PATH="$basedir\lib"
 
 & "$basedir\bin\llama-bench.exe" `
-    --mmap 0 -m $basedir\..\..\gguf\$model `
+    --load-mode none -m $basedir\..\..\gguf\$model `
     --poll 1000 -t 6 --cpu-mask 0xfc --cpu-strict 1 `
     --ubatch-size 1024 -ngl 99 --device $device $cli_opts

scripts/snapdragon/windows/run-cli.ps1

@@ -47,7 +47,7 @@ if ($null -ne $env:HB) {
 $env:ADSP_LIBRARY_PATH="$basedir\lib"
 
 & "$basedir\bin\llama-cli.exe" `
-    --no-mmap -m $basedir\..\..\gguf\$model `
+    --load-mode none -m $basedir\..\..\gguf\$model `
     --poll 1000 -t 6 --cpu-mask 0xfc --cpu-strict 1 `
     --ctx-size 8192 --ubatch-size 1024 -fa on `
     -ngl 99 --device $device $cli_opts

scripts/snapdragon/windows/run-completion.ps1

@@ -47,7 +47,7 @@ if ($null -ne $env:HB) {
 $env:ADSP_LIBRARY_PATH="$basedir\lib"
 
 & "$basedir\bin\llama-completion.exe" `
-    --no-mmap -m $basedir\..\..\gguf\$model `
+    --load-mode none -m $basedir\..\..\gguf\$model `
     --poll 1000 -t 6 --cpu-mask 0xfc --cpu-strict 1 `
     --ctx-size 8192 --ubatch-size 1024 -fa on `
     -ngl 99 -no-cnv --device $device $cli_opts

scripts/snapdragon/windows/run-mtmd.ps1

@@ -60,7 +60,7 @@ if ($null -ne $env:MTMD_DEVICE) {
 $env:ADSP_LIBRARY_PATH="$basedir\lib"
 
 & "$basedir\bin\llama-mtmd-cli.exe" `
-    --no-mmap -m $basedir\..\..\gguf\$model `
+    --load-mode none -m $basedir\..\..\gguf\$model `
     --mmproj $basedir\..\..\gguf\$mmproj `
     --image $basedir\..\..\gguf\$image `
     --poll 1000 -t 6 --cpu-mask 0xfc --cpu-strict 1 `

scripts/sync-ggml.last

@@ -1 +1 @@
-8846b79e66747bb9f68597420e95114c177315ce
+2d191b5dee1a591c41ee8a653ce42bfcd9c8716d

scripts/sync_vendor.py

@@ -5,7 +5,7 @@ import os
 import sys
 import subprocess
 
-HTTPLIB_VERSION = "refs/tags/v0.53.0"
+HTTPLIB_VERSION = "refs/tags/v0.53.1"
 
 vendor = {
     "https://github.com/nlohmann/json/releases/latest/download/json.hpp":     "vendor/nlohmann/json.hpp",

skills/code-review/SKILL.md

@@ -46,6 +46,8 @@ Mandatory on every review; any finding here is **blocking**. Rule of thumb: GGUF
 
 - **Sizes/counts from tensor dims:** validate before allocating. Products like `ne[i]*nb[i]`/nbytes can overflow on crafted dims into an undersized alloc then heap overflow. Overflow checks must run BEFORE the arithmetic they guard - padding/alignment macros wrap to 0 near `SIZE_MAX`, so a guard after the pad passes.
 - **GGUF strings/arrays:** cap declared lengths and element counts before using them to size a loop or buffer; validate element type and length before casting an array to a pointer or reading fixed indices (`[i+1]`, `[0..2]`).
+- **Element-type confusion:** casting `gguf_get_arr_data()` or `tensor->data` to `float *`/`int32_t *` needs an element-type check first (`gguf_get_kv_type() == GGUF_TYPE_ARRAY` then `gguf_get_arr_type()`; `type == GGML_TYPE_F32` for tensors). A `UINT8` array or `I8` tensor passes every length check, then gets read 4 bytes per element - a nearby length check is not a type check.
+- **Loaders:** `GGML_ASSERT` on a file-derived value aborts the process; throw instead where the caller already catches (vocab, model loader, clip).
 - **File-supplied counts indexing fixed arrays:** bound any count (e.g. layer/block count into a `LLAMA_MAX_*` array) before indexing; watch checks that only fire when an optional key is present.
 - **Declared vs actual array length:** check the declared length of a GGUF array against the count actually read, not just against a buffer size.
 - **Bounds comparisons:** flag narrowing casts (`size_t`->`int32_t`) and signed/unsigned mixing that can bypass a length check and copy past a buffer.

src/llama-adapter.cpp

@@ -396,8 +396,11 @@ static void llama_adapter_lora_init_impl(llama_model & model, const char * path_
         llama_file gguf_file(path_lora, "rb");
         std::vector<uint8_t> read_buf;
         auto set_tensor = [&](ggml_tensor * orig, ggml_tensor * dev) {
-            size_t offs = gguf_get_data_offset(ctx_gguf.get()) + gguf_get_tensor_offset(ctx_gguf.get(), gguf_find_tensor(ctx_gguf.get(), orig->name));
-            size_t size = ggml_nbytes(orig);
+            const size_t offs = gguf_get_data_offset(ctx_gguf.get()) + gguf_get_tensor_offset(ctx_gguf.get(), gguf_find_tensor(ctx_gguf.get(), orig->name));
+            const size_t size = ggml_nbytes(orig);
+            if (offs + size < offs || offs + size > gguf_file.size()) {
+                throw std::runtime_error(format("LoRA tensor '%s' data is not within the file bounds, file is corrupted or incomplete", orig->name));
+            }
             read_buf.resize(size);
             gguf_file.seek(offs, SEEK_SET);
             gguf_file.read_raw(read_buf.data(), size);

src/llama-arch.cpp

@@ -128,6 +128,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_SEED_OSS,         "seed_oss"         },
     { LLM_ARCH_GROVEMOE,         "grovemoe"         },
     { LLM_ARCH_APERTUS,          "apertus"          },
+    { LLM_ARCH_MINIMAX_01,       "minimax-01"       },
     { LLM_ARCH_MINIMAX_M2,       "minimax-m2"       },
     { LLM_ARCH_MINIMAX_M3,       "minimax-m3"       },
     { LLM_ARCH_COGVLM,           "cogvlm"           },
@@ -143,6 +144,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_LLAMA_EMBED,      "llama-embed"      },
     { LLM_ARCH_MAINCODER,        "maincoder"        },
     { LLM_ARCH_KIMI_LINEAR,      "kimi-linear"      },
+    { LLM_ARCH_KIMI_K3,          "kimi-k3"          },
     { LLM_ARCH_TALKIE,           "talkie"           },
     { LLM_ARCH_MELLUM,           "mellum"           },
     { LLM_ARCH_NANBEIGE,         "nanbeige"         },
@@ -186,6 +188,9 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
     { LLM_KV_FEATURES_LENGTH,                   "%s.features_length"                   },
     { LLM_KV_BLOCK_COUNT,                       "%s.block_count"                       },
     { LLM_KV_LEADING_DENSE_BLOCK_COUNT,         "%s.leading_dense_block_count"         },
+    { LLM_KV_ATTN_RES_BLOCK_SIZE,               "%s.attn_res.block_size"               },
+    { LLM_KV_ACTIVATION_SITU_BETA,              "%s.activation.situ_beta"              },
+    { LLM_KV_ACTIVATION_SITU_LINEAR_BETA,       "%s.activation.situ_linear_beta"       },
     { LLM_KV_FEED_FORWARD_LENGTH,               "%s.feed_forward_length"               },
     { LLM_KV_EXPERT_FEED_FORWARD_LENGTH,        "%s.expert_feed_forward_length"        },
     { LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, "%s.expert_shared_feed_forward_length" },
@@ -201,6 +206,7 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
     { LLM_KV_EXPERT_GROUP_USED_COUNT,           "%s.expert_group_used_count"           },
     { LLM_KV_EXPERT_WEIGHTS_SCALE,              "%s.expert_weights_scale"              },
     { LLM_KV_EXPERT_WEIGHTS_NORM,               "%s.expert_weights_norm"               },
+    { LLM_KV_EXPERT_LATENT_LENGTH,              "%s.expert_latent_length"              },
     { LLM_KV_EXPERT_GATING_FUNC,                "%s.expert_gating_func"                },
     { LLM_KV_EXPERT_GROUP_SCALE,                "%s.expert_group_scale"                },
     { LLM_KV_EXPERTS_PER_GROUP,                 "%s.experts_per_group"                 },
@@ -312,6 +318,7 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
     { LLM_KV_SSM_DT_B_C_RMS,     "%s.ssm.dt_b_c_rms"     },
 
     { LLM_KV_KDA_HEAD_DIM, "%s.kda.head_dim" },
+    { LLM_KV_KDA_GATE_LOWER_BOUND, "%s.kda.gate_lower_bound" },
 
     { LLM_KV_WKV_HEAD_SIZE, "%s.wkv.head_size" },
 
@@ -462,6 +469,13 @@ static const std::map<llm_tensor, const char *> LLM_TENSOR_NAMES = {
     { LLM_TENSOR_SSM_F_B,                                "blk.%d.ssm_f_b" },
     { LLM_TENSOR_SSM_BETA,                               "blk.%d.ssm_beta" },
     { LLM_TENSOR_SSM_G_A,                                "blk.%d.ssm_g_a" },
+    { LLM_TENSOR_SSM_G,                                  "blk.%d.ssm_g" },
+    { LLM_TENSOR_ATTN_RES_SCORE,                         "blk.%d.attn_res_score" },
+    { LLM_TENSOR_FFN_RES_SCORE,                          "blk.%d.ffn_res_score" },
+    { LLM_TENSOR_OUTPUT_RES_SCORE,                       "output_res_score" },
+    { LLM_TENSOR_FFN_ROUTED_DOWN,                        "blk.%d.ffn_routed_down" },
+    { LLM_TENSOR_FFN_ROUTED_UP,                          "blk.%d.ffn_routed_up" },
+    { LLM_TENSOR_FFN_ROUTED_NORM,                        "blk.%d.ffn_routed_norm" },
     { LLM_TENSOR_SSM_G_B,                                "blk.%d.ssm_g_b" },
     { LLM_TENSOR_SSM_NORM,                               "blk.%d.ssm_norm" },
     { LLM_TENSOR_ATTN_Q_A_NORM,                          "blk.%d.attn_q_a_norm" },
@@ -755,6 +769,13 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
     {LLM_TENSOR_SSM_F_B,                    {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
     {LLM_TENSOR_SSM_BETA,                   {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
     {LLM_TENSOR_SSM_G_A,                    {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_SSM_G,                      {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_ATTN_RES_SCORE,             {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+    {LLM_TENSOR_FFN_RES_SCORE,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+    {LLM_TENSOR_OUTPUT_RES_SCORE,           {LLM_TENSOR_LAYER_OUTPUT,    GGML_OP_MUL}},
+    {LLM_TENSOR_FFN_ROUTED_DOWN,            {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_FFN_ROUTED_UP,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_FFN_ROUTED_NORM,            {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
     {LLM_TENSOR_SSM_G_B,                    {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
     {LLM_TENSOR_TIME_MIX_LERP_X,            {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
     {LLM_TENSOR_TIME_MIX_LN,                {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
@@ -975,9 +996,11 @@ bool llm_arch_is_hybrid(const llm_arch & arch) {
         case LLM_ARCH_NEMOTRON_H_MOE:
         case LLM_ARCH_QWEN3NEXT:
         case LLM_ARCH_KIMI_LINEAR:
+        case LLM_ARCH_KIMI_K3:
         case LLM_ARCH_QWEN35:
         case LLM_ARCH_QWEN35MOE:
         case LLM_ARCH_DEEPSEEK4:
+        case LLM_ARCH_MINIMAX_01:
             return true;
         default:
             return false;
@@ -1033,10 +1056,12 @@ bool llm_arch_supports_sm_tensor(const llm_arch & arch) {
         case LLM_ARCH_GRANITE_HYBRID:
         case LLM_ARCH_LFM2:
         case LLM_ARCH_LFM2MOE:
+        case LLM_ARCH_MINIMAX_01:
         case LLM_ARCH_MINIMAX_M2:
         case LLM_ARCH_MINIMAX_M3:
         case LLM_ARCH_MISTRAL4:
         case LLM_ARCH_KIMI_LINEAR:
+        case LLM_ARCH_KIMI_K3:
         case LLM_ARCH_QWEN3TTS:
             return false;
         default:

src/llama-arch.h

@@ -145,6 +145,7 @@ enum llm_arch {
     LLM_ARCH_LLAMA_EMBED,
     LLM_ARCH_MAINCODER,
     LLM_ARCH_KIMI_LINEAR,
+    LLM_ARCH_KIMI_K3,
     LLM_ARCH_TALKIE,
     LLM_ARCH_MELLUM,
     LLM_ARCH_EAGLE3,
@@ -153,6 +154,7 @@ enum llm_arch {
     LLM_ARCH_NANBEIGE,
     LLM_ARCH_QWEN3TTS,
     LLM_ARCH_POCKETTTS,
+    LLM_ARCH_MINIMAX_01,
     LLM_ARCH_UNKNOWN,
 };
 
@@ -191,6 +193,9 @@ enum llm_kv {
     LLM_KV_FEATURES_LENGTH,
     LLM_KV_BLOCK_COUNT,
     LLM_KV_LEADING_DENSE_BLOCK_COUNT,
+    LLM_KV_ATTN_RES_BLOCK_SIZE,
+    LLM_KV_ACTIVATION_SITU_BETA,
+    LLM_KV_ACTIVATION_SITU_LINEAR_BETA,
     LLM_KV_FEED_FORWARD_LENGTH,
     LLM_KV_EXPERT_FEED_FORWARD_LENGTH,
     LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH,
@@ -206,6 +211,7 @@ enum llm_kv {
     LLM_KV_EXPERT_GROUP_USED_COUNT,
     LLM_KV_EXPERT_WEIGHTS_SCALE,
     LLM_KV_EXPERT_WEIGHTS_NORM,
+    LLM_KV_EXPERT_LATENT_LENGTH,
     LLM_KV_EXPERT_GATING_FUNC,
     LLM_KV_EXPERT_GROUP_SCALE,
     LLM_KV_EXPERTS_PER_GROUP,
@@ -317,6 +323,7 @@ enum llm_kv {
     LLM_KV_SSM_DT_B_C_RMS,
 
     LLM_KV_KDA_HEAD_DIM,
+    LLM_KV_KDA_GATE_LOWER_BOUND,
 
     LLM_KV_WKV_HEAD_SIZE,
 
@@ -491,6 +498,13 @@ enum llm_tensor {
     LLM_TENSOR_SSM_BETA,            // kimi: beta mixing coefficient and qwen3.5
     LLM_TENSOR_SSM_G_A,             // kimi: output gate projection A
     LLM_TENSOR_SSM_G_B,             // kimi: output gate projection B
+    LLM_TENSOR_SSM_G,               // kimi-k3: full-rank KDA gate
+    LLM_TENSOR_ATTN_RES_SCORE,      // kimi-k3: fused res_norm*res_proj (pre-attn)
+    LLM_TENSOR_FFN_RES_SCORE,       // kimi-k3: fused res_norm*res_proj (pre-ffn)
+    LLM_TENSOR_OUTPUT_RES_SCORE,    // kimi-k3: fused res_norm*res_proj (final)
+    LLM_TENSOR_FFN_ROUTED_DOWN,     // kimi-k3: latent MoE down
+    LLM_TENSOR_FFN_ROUTED_UP,       // kimi-k3: latent MoE up
+    LLM_TENSOR_FFN_ROUTED_NORM,     // kimi-k3: latent MoE norm
     LLM_TENSOR_TIME_MIX_W0,
     LLM_TENSOR_TIME_MIX_W1,
     LLM_TENSOR_TIME_MIX_W2,

src/llama-context.cpp

@@ -2293,13 +2293,17 @@ void llama_context::output_reorder() {
 
 uint32_t llama_context::graph_max_nodes(uint32_t n_tokens) const {
     uint32_t res;
-    if (model.arch == LLM_ARCH_QWEN3NEXT ||
+    if (model.arch == LLM_ARCH_KIMI_K3) {
+        // the n_tokens*40 budget below is exhausted at ubatch 3840
+        res = std::max<uint32_t>(n_tokens * 160, 64u * model.n_tensors());
+    } else if (model.arch == LLM_ARCH_QWEN3NEXT ||
         model.arch == LLM_ARCH_KIMI_LINEAR ||
         model.arch == LLM_ARCH_QWEN35 ||
         model.arch == LLM_ARCH_QWEN35MOE ||
         model.arch == LLM_ARCH_DEEPSEEK4 ||
         (model.arch == LLM_ARCH_DFLASH && model.hparams.dsv4_hc_mult > 0) ||
         model.arch == LLM_ARCH_NANBEIGE ||
+        model.arch == LLM_ARCH_MINIMAX_01 ||
         model.arch == LLM_ARCH_MINIMAX_M3) {
         res = std::max<uint32_t>(n_tokens * 40, 32u * model.n_tensors());
     } else {

src/llama-graph.cpp

@@ -1835,6 +1835,8 @@ ggml_tensor * llm_graph_context::build_ffn(
                 cur = ggml_reglu(ctx0, cur);
                 cb(cur, "ffn_reglu", il);
             } break;
+        case LLM_FFN_SITU:
+            GGML_ABORT("not yet supported");
         default:
             GGML_ABORT("fatal error");
     }
@@ -2174,6 +2176,21 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
                 cur = ggml_silu(ctx0, cur);
                 cb(cur, "ffn_moe_silu", il);
             } break;
+        case LLM_FFN_SITU:
+            {
+                // situ(gate, up) = beta*tanh(gate/beta)*sigmoid(gate) * lb*tanh(up/lb)
+                GGML_ASSERT(has_gate);
+                const float beta = hparams.situ_beta;
+                const float lb   = hparams.situ_linear_beta;
+
+                ggml_tensor * act = ggml_scale(ctx0, ggml_tanh(ctx0, ggml_scale(ctx0, cur, 1.0f/beta)), beta);
+                act = ggml_mul(ctx0, act, ggml_sigmoid(ctx0, cur));
+                if (lb > 0.0f) {
+                    up = ggml_scale(ctx0, ggml_tanh(ctx0, ggml_scale(ctx0, up, 1.0f/lb)), lb);
+                }
+                cur = ggml_mul(ctx0, act, up);
+                cb(cur, "ffn_moe_situ", il);
+            } break;
         case LLM_FFN_GELU:
             if (has_gate) {
                 cur = ggml_geglu_split(ctx0, cur, up);

src/llama-graph.h

@@ -59,6 +59,7 @@ enum llm_ffn_op_type : int {
     LLM_FFN_GEGLU,
     LLM_FFN_REGLU,
     LLM_FFN_SWIGLU_OAI_MOE,
+    LLM_FFN_SITU,           // kimi-k3
 };
 
 enum llm_ffn_gate_type {

src/llama-hparams.cpp

@@ -217,6 +217,13 @@ uint32_t llama_hparams::n_embd_s() const {
         return n_embd_head_kda * n_embd_head_kda * n_head();  // 128 * 128 * 32 = 524288
     }
 
+    if (n_embd_head_la != 0) {
+        // for MiniMax-Text-01 linear attention layers
+        // Full recurrent state: head_dim * head_dim * n_head
+        // tensor shape for linear attention: [head_dim, head_dim, n_head]
+        return n_embd_head_la * n_embd_head_la * n_head();  // 128 * 128 * 64 = 1048576
+    }
+
     // corresponds to Mamba's ssm_states size
     return ssm_d_state * ssm_d_inner;
 }

src/llama-hparams.h

@@ -4,10 +4,11 @@
 
 #include <array>
 #include <cassert>
+#include <cmath>
 
 // bump if necessary
 #define LLAMA_MAX_LAYERS  512
-#define LLAMA_MAX_EXPERTS 512 // Qwen3 Next
+#define LLAMA_MAX_EXPERTS 1024 // Kimi K3
 
 enum llama_expert_gating_func_type {
     LLAMA_EXPERT_GATING_FUNC_TYPE_NONE           = 0,
@@ -164,9 +165,19 @@ struct llama_hparams {
     uint32_t ssm_dt_rank = 0;
     uint32_t ssm_n_group = 0;
 
+    // for MiniMax-Text-01 linear attention
+    uint32_t n_embd_head_la = 0;
+
     // for Kimi Linear KDA
     uint32_t n_embd_head_kda = 0;
 
+    // kimi-k3
+    uint32_t n_expert_latent      = 0;      // routed_expert_hidden_size (0 = experts run at n_embd)
+    uint32_t attn_res_block_size  = 0;      // 0 = no cross-layer attention residuals
+    float    kda_gate_lower_bound = -INFINITY;
+    float    situ_beta            = 1.0f;
+    float    situ_linear_beta     = 0.0f;   // 0 = no linear-beta transform on the up branch
+
     bool ssm_dt_b_c_rms = false;
 
     float f_clamp_kqv      = 0.0f;

src/llama-model-loader.cpp

@@ -316,15 +316,19 @@ namespace GGUFMeta {
         struct GGUFMeta::ArrayInfo arr_info =
             GGUFMeta::GKV<GGUFMeta::ArrayInfo>::get_kv(ctx, kid);
 
+        bool type_ok = false;
         switch (arr_info.gt) {
             case GGUF_TYPE_UINT32:
-            case GGUF_TYPE_INT32:   GGML_ASSERT((std::is_same<T,     int32_t>::value) ||
-                                                (std::is_same<T,    uint32_t>::value)); break;
-            case GGUF_TYPE_FLOAT32: GGML_ASSERT((std::is_same<T,       float>::value)); break;
-            case GGUF_TYPE_STRING:  GGML_ASSERT((std::is_same<T, std::string>::value)); break;
+            case GGUF_TYPE_INT32:   type_ok = (std::is_same<T,     int32_t>::value) ||
+                                              (std::is_same<T,    uint32_t>::value); break;
+            case GGUF_TYPE_FLOAT32: type_ok = (std::is_same<T,       float>::value); break;
+            case GGUF_TYPE_STRING:  type_ok = (std::is_same<T, std::string>::value); break;
             default:
                 throw std::runtime_error(format("%s is not a string/float32/uint32/int32 array", key.c_str()));
         }
+        if (!type_ok) {
+            throw std::runtime_error(format("%s has wrong array element type %s", key.c_str(), gguf_type_name(arr_info.gt)));
+        }
 
         if constexpr (std::is_same<T, std::string>::value) {
             const size_t n_items = gguf_get_arr_n(ctx, kid);
@@ -357,16 +361,20 @@ namespace GGUFMeta {
         struct GGUFMeta::ArrayInfo arr_info =
             GGUFMeta::GKV<GGUFMeta::ArrayInfo>::get_kv(ctx, kid);
 
+        bool type_ok = false;
         switch (arr_info.gt) {
             case GGUF_TYPE_BOOL:
             case GGUF_TYPE_UINT32:
-            case GGUF_TYPE_INT32:   GGML_ASSERT((std::is_same<T,     int32_t>::value) ||
-                                                (std::is_same<T,    uint32_t>::value)); break;
-            case GGUF_TYPE_FLOAT32: GGML_ASSERT((std::is_same<T,       float>::value)); break;
-            case GGUF_TYPE_STRING:  GGML_ASSERT((std::is_same<T, std::string>::value)); break;
+            case GGUF_TYPE_INT32:   type_ok = (std::is_same<T,     int32_t>::value) ||
+                                              (std::is_same<T,    uint32_t>::value); break;
+            case GGUF_TYPE_FLOAT32: type_ok = (std::is_same<T,       float>::value); break;
+            case GGUF_TYPE_STRING:  type_ok = (std::is_same<T, std::string>::value); break;
             default:
                 throw std::runtime_error(format("%s is not a string/float32/uint32/int32 array", key.c_str()));
         }
+        if (!type_ok) {
+            throw std::runtime_error(format("%s has wrong array element type %s", key.c_str(), gguf_type_name(arr_info.gt)));
+        }
 
         if (arr_info.length > N_MAX) {
             throw std::runtime_error(format("array length %u for key %s exceeds max %u", (uint32_t) arr_info.length, key.c_str(), (uint32_t) N_MAX));
@@ -1178,7 +1186,7 @@ struct ggml_tensor * llama_model_loader::create_tensor(
                         if (use_mmap) {
                             static std::once_flag once;
                             std::call_once(once, [] {
-                                LLAMA_LOG_WARN("llama_model_loader: tensor overrides to CPU are used with mmap enabled - consider using --no-mmap for better performance\n");
+                                LLAMA_LOG_WARN("llama_model_loader: tensor overrides to CPU are used with mmap enabled - consider using --load-mode none for better performance\n");
                             });
                         }
                     } else {

src/llama-model-saver.cpp

@@ -213,6 +213,7 @@ void llama_model_saver::add_kv_from_model() {
     add_kv(LLM_KV_LEADING_DENSE_BLOCK_COUNT,         hparams.n_layer_dense_lead);
     add_kv(LLM_KV_FEED_FORWARD_LENGTH,               hparams.n_ff_arr, true);
     add_kv(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,        hparams.n_ff_exp);
+    add_kv(LLM_KV_EXPERT_LATENT_LENGTH,              hparams.n_expert_latent);
     add_kv(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp);
     add_kv(LLM_KV_EXPERT_CHUNK_FEED_FORWARD_LENGTH,  hparams.n_ff_chexp);
     add_kv(LLM_KV_SWIGLU_CLAMP_EXP,                  hparams.swiglu_clamp_exp);
@@ -319,6 +320,7 @@ void llama_model_saver::add_kv_from_model() {
     add_kv(LLM_KV_SSM_DT_B_C_RMS,                    hparams.ssm_dt_b_c_rms);
 
     add_kv(LLM_KV_KDA_HEAD_DIM,                      hparams.n_embd_head_kda);
+    add_kv(LLM_KV_KDA_GATE_LOWER_BOUND,              hparams.kda_gate_lower_bound);
 
     add_kv(LLM_KV_WKV_HEAD_SIZE,                     hparams.wkv_head_size);
 
@@ -376,6 +378,10 @@ void llama_model_saver::add_kv_from_model() {
     add_kv(LLM_KV_XIELU_BETA,                        hparams.xielu_beta);
     add_kv(LLM_KV_XIELU_EPS,                         hparams.xielu_eps);
 
+    add_kv(LLM_KV_ATTN_RES_BLOCK_SIZE,               hparams.attn_res_block_size);
+    add_kv(LLM_KV_ACTIVATION_SITU_BETA,              hparams.situ_beta);
+    add_kv(LLM_KV_ACTIVATION_SITU_LINEAR_BETA,       hparams.situ_linear_beta);
+
     // deprecated
     // add_kv(LLM_KV_TOKENIZER_PREFIX_ID,               ???);
     // add_kv(LLM_KV_TOKENIZER_SUFFIX_ID,               ???);
@@ -403,6 +409,7 @@ void llama_model_saver::add_tensors_from_model() {
     add_tensor(model->output_norm_enc);
     add_tensor(model->output_s);
     add_tensor(model->output_in_s);
+    add_tensor(model->output_res_score);
     add_tensor(model->cls);
     add_tensor(model->cls_b);
     add_tensor(model->cls_out);

src/llama-model.cpp

@@ -296,6 +296,8 @@ static llama_model * llama_model_mapping(llm_arch arch, const llama_model_params
             return new llama_model_grovemoe(params);
         case LLM_ARCH_APERTUS:
             return new llama_model_apertus(params);
+        case LLM_ARCH_MINIMAX_01:
+            return new llama_model_minimax_01(params);
         case LLM_ARCH_MINIMAX_M2:
             return new llama_model_minimax_m2(params);
         case LLM_ARCH_MINIMAX_M3:
@@ -320,6 +322,8 @@ static llama_model * llama_model_mapping(llm_arch arch, const llama_model_params
             return new llama_model_mimo2(params);
         case LLM_ARCH_KIMI_LINEAR:
             return new llama_model_kimi_linear(params);
+        case LLM_ARCH_KIMI_K3:
+            return new llama_model_kimi_k3(params);
         case LLM_ARCH_STEP35:
             return new llama_model_step35(params);
         default:
@@ -798,6 +802,7 @@ const char * llm_type_name(llm_type type) {
         case LLM_TYPE_290B:          return "290B";
         case LLM_TYPE_314B:          return "314B";
         case LLM_TYPE_405B:          return "405B";
+        case LLM_TYPE_456B:          return "456B";
         case LLM_TYPE_671B:          return "671B";
         case LLM_TYPE_SMALL:         return "0.1B";
         case LLM_TYPE_MEDIUM:        return "0.4B";
@@ -842,6 +847,7 @@ const char * llm_type_name(llm_type type) {
         case LLM_TYPE_397B_A17B:     return "397B.A17B";
         case LLM_TYPE_685B_A37B:     return "685B.A37B";
         case LLM_TYPE_744B_A40B:     return "744B.A40B";
+        case LLM_TYPE_2_8T_A50B:     return "2.8T.A50B";
         case LLM_TYPE_E2B:           return "E2B";
         case LLM_TYPE_E4B:           return "E4B";
         default:                     return "?B";
@@ -2283,7 +2289,7 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
                         filter_recr = [&](uint32_t il) {
                             return hparams.is_recr(il) && hparams.n_ff(il) == 0;
                         };
-                    } else if (arch == LLM_ARCH_QWEN3NEXT || arch == LLM_ARCH_QWEN35 || arch == LLM_ARCH_QWEN35MOE) {
+                    } else if (arch == LLM_ARCH_QWEN3NEXT || arch == LLM_ARCH_QWEN35 || arch == LLM_ARCH_QWEN35MOE || arch == LLM_ARCH_MINIMAX_01) {
                         filter_attn = [&](uint32_t il) {
                             return il < hparams.n_layer() && !hparams.is_recr(il);
                         };
@@ -2599,6 +2605,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
         case LLM_ARCH_NEMOTRON_H:
         case LLM_ARCH_NEMOTRON_H_MOE:
         case LLM_ARCH_KIMI_LINEAR:
+        case LLM_ARCH_KIMI_K3:
             return LLAMA_ROPE_TYPE_NONE;
 
         // use what we call a normal RoPE, operating on pairs of consecutive head values
@@ -2704,6 +2711,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
         case LLM_ARCH_SEED_OSS:
         case LLM_ARCH_GROVEMOE:
         case LLM_ARCH_APERTUS:
+        case LLM_ARCH_MINIMAX_01:
         case LLM_ARCH_MINIMAX_M2:
         case LLM_ARCH_MINIMAX_M3:
         case LLM_ARCH_COGVLM:

src/llama-model.h

@@ -99,6 +99,7 @@ enum llm_type {
     LLM_TYPE_290B,
     LLM_TYPE_314B,
     LLM_TYPE_405B,
+    LLM_TYPE_456B,
     LLM_TYPE_671B,
     LLM_TYPE_SMALL,
     LLM_TYPE_MEDIUM,
@@ -143,6 +144,7 @@ enum llm_type {
     LLM_TYPE_397B_A17B, // Qwen3.5
     LLM_TYPE_685B_A37B, // DeepSeek V3.2
     LLM_TYPE_744B_A40B, // GLM-5
+    LLM_TYPE_2_8T_A50B, // Kimi-K3
     LLM_TYPE_E2B,
     LLM_TYPE_E4B,
 };
@@ -271,6 +273,7 @@ struct llama_layer {
     struct ggml_tensor * wv        = nullptr;
     struct ggml_tensor * wo        = nullptr;
     struct ggml_tensor * wqkv      = nullptr;
+    struct ggml_tensor * wg        = nullptr;
     struct ggml_tensor * wq_a      = nullptr;
     struct ggml_tensor * wq_b      = nullptr;
     struct ggml_tensor * wkv_a_mqa = nullptr;
@@ -528,6 +531,14 @@ struct llama_layer {
     struct ggml_tensor * ssm_g_b    = nullptr;
     struct ggml_tensor * ssm_o_norm = nullptr;
 
+    // kimi-k3
+    struct ggml_tensor * ssm_g           = nullptr; // full-rank KDA gate (replaces ssm_g_a/ssm_g_b)
+    struct ggml_tensor * attn_res_score  = nullptr; // fused res_norm*res_proj, pre-attention
+    struct ggml_tensor * ffn_res_score   = nullptr; // fused res_norm*res_proj, pre-FFN
+    struct ggml_tensor * ffn_routed_down = nullptr; // latent MoE: n_embd -> n_expert_latent
+    struct ggml_tensor * ffn_routed_up   = nullptr; // latent MoE: n_expert_latent -> n_embd
+    struct ggml_tensor * ffn_routed_norm = nullptr;
+
     // DSA (deepseek sparse attention)
     struct ggml_tensor * indexer_k_norm   = nullptr;
     struct ggml_tensor * indexer_k_norm_b = nullptr;
@@ -587,6 +598,7 @@ struct llama_model {
     struct ggml_tensor * tok_norm_b = nullptr;
 
     struct ggml_tensor * output_norm     = nullptr;
+    struct ggml_tensor * output_res_score = nullptr; // kimi-k3: final cross-layer residual mix
     struct ggml_tensor * output_norm_b   = nullptr;
     struct ggml_tensor * output          = nullptr;
     struct ggml_tensor * output_b        = nullptr;

src/llama-vocab.cpp

@@ -1989,6 +1989,10 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
                 // Kimi-K2 doesn't need merges, skip
                 LLAMA_LOG_INFO("%s: Kimi-K2 tokenizer detected, skipping BPE merges\n", __func__);
             } else {
+                if (gguf_get_kv_type(ctx, merges_keyidx) != GGUF_TYPE_ARRAY ||
+                    gguf_get_arr_type(ctx, merges_keyidx) != GGUF_TYPE_STRING) {
+                    throw std::runtime_error(format("invalid gguf type for %s", kv(LLM_KV_TOKENIZER_MERGES).c_str()));
+                }
                 const int n_merges = gguf_get_arr_n(ctx, merges_keyidx);
                 for (int i = 0; i < n_merges; i++) {
                     const std::string word = gguf_get_arr_str(ctx, merges_keyidx, i);
@@ -2028,8 +2032,13 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
 
             const int precompiled_charsmap_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP).c_str());
             if (precompiled_charsmap_keyidx != -1) {
+                if (gguf_get_kv_type(ctx, precompiled_charsmap_keyidx) != GGUF_TYPE_ARRAY) {
+                    throw std::runtime_error(format("invalid gguf type for %s", kv(LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP).c_str()));
+                }
                 const gguf_type pc_type = gguf_get_arr_type(ctx, precompiled_charsmap_keyidx);
-                GGML_ASSERT(pc_type == GGUF_TYPE_INT8 || pc_type == GGUF_TYPE_UINT8);
+                if (pc_type != GGUF_TYPE_INT8 && pc_type != GGUF_TYPE_UINT8) {
+                    throw std::runtime_error(format("invalid gguf type for %s", kv(LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP).c_str()));
+                }
 
                 const size_t n_precompiled_charsmap = gguf_get_arr_n(ctx, precompiled_charsmap_keyidx);
                 const char * pc = (const char *) gguf_get_arr_data(ctx, precompiled_charsmap_keyidx);
@@ -2081,6 +2090,10 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
                 throw std::runtime_error("cannot find tokenizer merges in model file\n");
             }
             {
+                if (gguf_get_kv_type(ctx, merges_keyidx) != GGUF_TYPE_ARRAY ||
+                    gguf_get_arr_type(ctx, merges_keyidx) != GGUF_TYPE_STRING) {
+                    throw std::runtime_error(format("invalid gguf type for %s", kv(LLM_KV_TOKENIZER_MERGES).c_str()));
+                }
                 const int n_merges = gguf_get_arr_n(ctx, merges_keyidx);
                 for (int i = 0; i < n_merges; i++) {
                     const std::string word = gguf_get_arr_str(ctx, merges_keyidx, i);
@@ -2407,11 +2420,20 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
         throw std::runtime_error("cannot find tokenizer vocab in model file\n");
     }
 
+    if (gguf_get_kv_type(ctx, token_idx) != GGUF_TYPE_ARRAY ||
+        gguf_get_arr_type(ctx, token_idx) != GGUF_TYPE_STRING) {
+        throw std::runtime_error(format("invalid gguf type for %s", kv(LLM_KV_TOKENIZER_LIST).c_str()));
+    }
+
     const uint32_t n_tokens = gguf_get_arr_n(ctx, token_idx);
 
     const float * scores = nullptr;
     const int score_idx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_SCORES).c_str());
     if (score_idx != -1) {
+        if (gguf_get_kv_type(ctx, score_idx) != GGUF_TYPE_ARRAY ||
+            gguf_get_arr_type(ctx, score_idx) != GGUF_TYPE_FLOAT32) {
+            throw std::runtime_error(format("invalid gguf type for %s", kv(LLM_KV_TOKENIZER_SCORES).c_str()));
+        }
         const uint32_t n_scores = gguf_get_arr_n(ctx, score_idx);
         if (n_scores < n_tokens) {
             throw std::runtime_error("Index out of array bounds for scores (" + std::to_string(n_scores) + " < " + std::to_string(n_tokens) + ")\n");
@@ -2422,6 +2444,10 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
     const int * toktypes = nullptr;
     const int toktype_idx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_TOKEN_TYPE).c_str());
     if (toktype_idx != -1) {
+        if (gguf_get_kv_type(ctx, toktype_idx) != GGUF_TYPE_ARRAY ||
+            gguf_get_arr_type(ctx, toktype_idx) != GGUF_TYPE_INT32) {
+            throw std::runtime_error(format("invalid gguf type for %s", kv(LLM_KV_TOKENIZER_TOKEN_TYPE).c_str()));
+        }
         const uint32_t n_toktypes = gguf_get_arr_n(ctx, toktype_idx);
         if (n_toktypes < n_tokens) {
             throw std::runtime_error("Index out of array bounds for toktypes (" + std::to_string(n_toktypes) + " < " + std::to_string(n_tokens) + ")\n");
@@ -2584,6 +2610,10 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
         {
             const int suppress_idx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_SUPPRESS_TOKENS).c_str());
             if (suppress_idx != -1) {
+                if (gguf_get_kv_type(ctx, suppress_idx) != GGUF_TYPE_ARRAY ||
+                    gguf_get_arr_type(ctx, suppress_idx) != GGUF_TYPE_INT32) {
+                    throw std::runtime_error(format("invalid gguf type for %s", kv(LLM_KV_TOKENIZER_SUPPRESS_TOKENS).c_str()));
+                }
                 const int n = gguf_get_arr_n(ctx, suppress_idx);
                 const int32_t * data = (const int32_t *) gguf_get_arr_data(ctx, suppress_idx);
                 // drop out-of-range ids

src/models/deepseek32.cpp

@@ -180,10 +180,11 @@ llama_model_deepseek32::graph::graph(const llama_model & model, const llm_graph_
 
     const int64_t n_indexer_head = hparams.indexer_n_head;
     const int64_t n_embd_indexer_head = hparams.indexer_head_size;
-    const int64_t n_embd_indexer_head_rope = hparams.n_rot();
-    const int64_t n_embd_indexer_head_nope = n_embd_indexer_head - n_embd_indexer_head_rope;
     const uint32_t n_indexer_top_k = hparams.indexer_top_k;
 
+    // the indexer head layous is [rope | nope]
+    GGML_ASSERT(hparams.n_rot() <= n_embd_indexer_head);
+
     const uint32_t kv_lora_rank = hparams.n_lora_kv;
 
     // We have to pre-scale kq_scale and attn_factor to make the YaRN RoPE work correctly.
@@ -233,28 +234,11 @@ llama_model_deepseek32::graph::graph(const llama_model & model, const llm_graph_
                 ggml_tensor * indexer_q = ggml_mul_mat(ctx0, model.layers[il].indexer_attn_q_b, qr);
                 cb(indexer_q, "indexer_q", il);
 
-                // split into {n_embd_indexer_head_rope, n_indexer_head, n_tokens}
-                ggml_tensor * indexer_q_pe =
-                    ggml_view_3d(ctx0, indexer_q, n_embd_indexer_head_rope, n_indexer_head, n_tokens,
-                                 ggml_row_size(indexer_q->type, n_embd_indexer_head),
-                                 ggml_row_size(indexer_q->type, n_embd_indexer_head) * n_indexer_head, 0);
-                cb(indexer_q_pe, "indexer_q_pe", il);
-
-                // and {n_embd_indexer_head_nope, n_indexer_head, n_tokens}
-                ggml_tensor * indexer_q_nope =
-                    ggml_view_3d(ctx0, indexer_q, n_embd_indexer_head_nope, n_indexer_head, n_tokens,
-                                 ggml_row_size(indexer_q->type, n_embd_indexer_head),
-                                 ggml_row_size(indexer_q->type, n_embd_indexer_head) * n_indexer_head,
-                                 ggml_row_size(indexer_q->type, n_embd_indexer_head_nope));
-                cb(indexer_q_nope, "indexer_q_nope", il);
-
-                indexer_q_pe = ggml_rope_ext(ctx0, indexer_q_pe, inp_pos, nullptr, n_rot,
+                // {n_embd_indexer_head, n_indexer_head, n_tokens}
+                indexer_q = ggml_reshape_3d(ctx0, indexer_q, n_embd_indexer_head, n_indexer_head, n_tokens);
+                indexer_q = ggml_rope_ext(ctx0, indexer_q, inp_pos, nullptr, n_rot,
                                      LLAMA_ROPE_TYPE_NEOX, n_ctx_orig, freq_base, freq_scale,
                                      ext_factor, attn_factor, beta_fast, beta_slow);
-                cb(indexer_q_pe, "indexer_q_pe", il);
-
-                // {n_embd_indexer_head_rope + n_embd_indexer_head_nope, n_head, n_tokens}
-                indexer_q = ggml_concat(ctx0, indexer_q_pe, indexer_q_nope, 0);
                 cb(indexer_q, "indexer_q", il);
 
                 ggml_tensor * indexer_k = ggml_mul_mat(ctx0, model.layers[il].indexer_attn_k, cur);
@@ -263,28 +247,11 @@ llama_model_deepseek32::graph::graph(const llama_model & model, const llm_graph_
                 indexer_k = build_norm(indexer_k, model.layers[il].indexer_k_norm, model.layers[il].indexer_k_norm_b, LLM_NORM, il);
                 cb(indexer_k, "indexer_k", il);
 
-                // split into {n_embd_indexer_head_rope, 1, n_tokens}
-                ggml_tensor * indexer_k_pe =
-                    ggml_view_3d(ctx0, indexer_k, n_embd_indexer_head_rope, 1, n_tokens,
-                                 ggml_row_size(indexer_k->type, n_embd_indexer_head),
-                                 ggml_row_size(indexer_k->type, n_embd_indexer_head) * 1, 0);
-                cb(indexer_k_pe, "indexer_k_pe", il);
-
-                // and {n_embd_indexer_head_nope, 1, n_tokens}
-                ggml_tensor * indexer_k_nope =
-                    ggml_view_3d(ctx0, indexer_k, n_embd_indexer_head_nope, 1, n_tokens,
-                                 ggml_row_size(indexer_k->type, n_embd_indexer_head),
-                                 ggml_row_size(indexer_k->type, n_embd_indexer_head) * 1,
-                                 ggml_row_size(indexer_k->type, n_embd_indexer_head_nope));
-                cb(indexer_k_nope, "indexer_k_nope", il);
-
-                indexer_k_pe = ggml_rope_ext(ctx0, indexer_k_pe, inp_pos, nullptr, n_rot,
+                // {n_embd_indexer_head, 1, n_tokens}
+                indexer_k = ggml_reshape_3d(ctx0, indexer_k, n_embd_indexer_head, 1, n_tokens);
+                indexer_k = ggml_rope_ext(ctx0, indexer_k, inp_pos, nullptr, n_rot,
                                      LLAMA_ROPE_TYPE_NEOX, n_ctx_orig, freq_base, freq_scale,
                                      ext_factor, attn_factor, beta_fast, beta_slow);
-                cb(indexer_k_pe, "indexer_k_pe", il);
-
-                // {n_embd_indexer_head_rope + n_embd_indexer_head_nope, 1, n_tokens}
-                indexer_k = ggml_concat(ctx0, indexer_k_pe, indexer_k_nope, 0);
                 cb(indexer_k, "indexer_k", il);
 
                 // perform Hadamard transform on indexer q and k

src/models/dflash.cpp

@@ -43,6 +43,8 @@ void llama_model_dflash::load_arch_hparams(llama_model_loader & ml) {
         ml.get_key(LLM_KV_HYPER_CONNECTION_EPSILON,             hparams.dsv4_hc_eps);
         ml.get_arr(LLM_KV_ATTENTION_COMPRESS_RATIOS,            hparams.dsv4_compress_ratios, false);
 
+        GGML_ASSERT(hparams.dsv4_o_group_count > 0); // avoid div by zero
+
         if (hparams.expert_gating_func != LLAMA_EXPERT_GATING_FUNC_TYPE_SQRT_SOFTPLUS) {
             throw std::runtime_error("DSpark DSV4 draft expects sqrtsoftplus MoE scoring");
         }

src/models/glm-dsa.cpp

@@ -216,10 +216,11 @@ llama_model_glm_dsa::graph::graph(const llama_model & model, const llm_graph_par
 
     const int64_t n_indexer_head = hparams.indexer_n_head;
     const int64_t n_embd_indexer_head = hparams.indexer_head_size;
-    const int64_t n_embd_indexer_head_rope = hparams.n_rot();
-    const int64_t n_embd_indexer_head_nope = n_embd_indexer_head - n_embd_indexer_head_rope;
     const uint32_t n_indexer_top_k = hparams.indexer_top_k;
 
+    // the indexer head layout is [rope | nope]
+    GGML_ASSERT(hparams.n_rot() <= n_embd_indexer_head);
+
     const uint32_t kv_lora_rank = hparams.n_lora_kv;
 
     // We have to pre-scale kq_scale and attn_factor to make the YaRN RoPE work correctly.
@@ -273,28 +274,11 @@ llama_model_glm_dsa::graph::graph(const llama_model & model, const llm_graph_par
                 ggml_tensor * indexer_q = ggml_mul_mat(ctx0, model.layers[il].indexer_attn_q_b, qr);
                 cb(indexer_q, "indexer_q", il);
 
-                // split into {n_embd_indexer_head_rope, n_indexer_head, n_tokens}
-                ggml_tensor * indexer_q_pe =
-                    ggml_view_3d(ctx0, indexer_q, n_embd_indexer_head_rope, n_indexer_head, n_tokens,
-                                 ggml_row_size(indexer_q->type, n_embd_indexer_head),
-                                 ggml_row_size(indexer_q->type, n_embd_indexer_head) * n_indexer_head, 0);
-                cb(indexer_q_pe, "indexer_q_pe", il);
-
-                // and {n_embd_indexer_head_nope, n_indexer_head, n_tokens}
-                ggml_tensor * indexer_q_nope =
-                    ggml_view_3d(ctx0, indexer_q, n_embd_indexer_head_nope, n_indexer_head, n_tokens,
-                                 ggml_row_size(indexer_q->type, n_embd_indexer_head),
-                                 ggml_row_size(indexer_q->type, n_embd_indexer_head) * n_indexer_head,
-                                 ggml_row_size(indexer_q->type, n_embd_indexer_head_nope));
-                cb(indexer_q_nope, "indexer_q_nope", il);
-
-                indexer_q_pe = ggml_rope_ext(ctx0, indexer_q_pe, inp_pos, nullptr, n_rot,
+                // {n_embd_indexer_head, n_indexer_head, n_tokens}
+                indexer_q = ggml_reshape_3d(ctx0, indexer_q, n_embd_indexer_head, n_indexer_head, n_tokens);
+                indexer_q = ggml_rope_ext(ctx0, indexer_q, inp_pos, nullptr, n_rot,
                                      LLAMA_ROPE_TYPE_NORM, n_ctx_orig, freq_base, freq_scale,
                                      ext_factor, attn_factor, beta_fast, beta_slow);
-                cb(indexer_q_pe, "indexer_q_pe", il);
-
-                // {n_embd_indexer_head_rope + n_embd_indexer_head_nope, n_head, n_tokens}
-                indexer_q = ggml_concat(ctx0, indexer_q_pe, indexer_q_nope, 0);
                 cb(indexer_q, "indexer_q", il);
 
                 ggml_tensor * indexer_k = ggml_mul_mat(ctx0, model.layers[il].indexer_attn_k, cur);
@@ -303,28 +287,11 @@ llama_model_glm_dsa::graph::graph(const llama_model & model, const llm_graph_par
                 indexer_k = build_norm(indexer_k, model.layers[il].indexer_k_norm, model.layers[il].indexer_k_norm_b, LLM_NORM, il);
                 cb(indexer_k, "indexer_k", il);
 
-                // split into {n_embd_indexer_head_rope, 1, n_tokens}
-                ggml_tensor * indexer_k_pe =
-                    ggml_view_3d(ctx0, indexer_k, n_embd_indexer_head_rope, 1, n_tokens,
-                                 ggml_row_size(indexer_k->type, n_embd_indexer_head),
-                                 ggml_row_size(indexer_k->type, n_embd_indexer_head) * 1, 0);
-                cb(indexer_k_pe, "indexer_k_pe", il);
-
-                // and {n_embd_indexer_head_nope, 1, n_tokens}
-                ggml_tensor * indexer_k_nope =
-                    ggml_view_3d(ctx0, indexer_k, n_embd_indexer_head_nope, 1, n_tokens,
-                                 ggml_row_size(indexer_k->type, n_embd_indexer_head),
-                                 ggml_row_size(indexer_k->type, n_embd_indexer_head) * 1,
-                                 ggml_row_size(indexer_k->type, n_embd_indexer_head_nope));
-                cb(indexer_k_nope, "indexer_k_nope", il);
-
-                indexer_k_pe = ggml_rope_ext(ctx0, indexer_k_pe, inp_pos, nullptr, n_rot,
+                // {n_embd_indexer_head, 1, n_tokens}
+                indexer_k = ggml_reshape_3d(ctx0, indexer_k, n_embd_indexer_head, 1, n_tokens);
+                indexer_k = ggml_rope_ext(ctx0, indexer_k, inp_pos, nullptr, n_rot,
                                      LLAMA_ROPE_TYPE_NORM, n_ctx_orig, freq_base, freq_scale,
                                      ext_factor, attn_factor, beta_fast, beta_slow);
-                cb(indexer_k_pe, "indexer_k_pe", il);
-
-                // {n_embd_indexer_head_rope + n_embd_indexer_head_nope, 1, n_tokens}
-                indexer_k = ggml_concat(ctx0, indexer_k_pe, indexer_k_nope, 0);
                 cb(indexer_k, "indexer_k", il);
 
                 // perform Hadamard transform on indexer q and k

src/models/kimi-k3.cpp

@@ -0,0 +1,614 @@
+#include "models.h"
+#include "llama-memory-recurrent.h"
+
+//
+// Kimi-K3 text model: hybrid KDA (linear) + MLA (full) attention, as in kimi-linear.
+// Parts that kimi-linear does not have:
+//   1. cross-layer residual attention  (attn_res_block_size)
+//   2. latent MoE                      (routed experts run at n_expert_latent)
+//   3. situ activation                 (replaces SwiGLU everywhere)
+//   4. MLA output gate                 (sigmoid gate before o_proj)
+//   5. full-rank KDA gate              (single ssm_g instead of ssm_g_a/ssm_g_b)
+//
+
+void llama_model_kimi_k3::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH_MLA,    hparams.n_embd_head_k_mla_impl);
+    ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH_MLA,  hparams.n_embd_head_v_mla_impl);
+    ml.get_key(LLM_KV_ATTENTION_Q_LORA_RANK,       hparams.n_lora_q, false);
+    ml.get_key(LLM_KV_ATTENTION_KV_LORA_RANK,      hparams.n_lora_kv);
+    ml.get_key(LLM_KV_SSM_CONV_KERNEL,             hparams.ssm_d_conv);
+    ml.get_key(LLM_KV_KDA_HEAD_DIM,                hparams.n_embd_head_kda);
+    ml.get_key(LLM_KV_KDA_GATE_LOWER_BOUND,        hparams.kda_gate_lower_bound, false);
+
+    // the MLA cache holds the compressed latent
+    // set it here too, as older GGUFs have no value_length key
+    hparams.n_embd_head_v_full = hparams.n_lora_kv;
+
+    // n_head_kv == 0 marks a KDA (recurrent) layer, as in kimi-linear
+    for (uint32_t i = 0; i < hparams.n_layer(); ++i) {
+        hparams.is_recr_impl[i] = hparams.n_head_kv(i) == 0;
+    }
+
+    ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
+    ml.get_key(LLM_KV_EXPERT_SHARED_COUNT,        hparams.n_expert_shared);
+    ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT,  hparams.n_layer_dense_lead, false);
+    ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE,       hparams.expert_weights_scale, false);
+    ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM,        hparams.expert_weights_norm, false);
+    ml.get_key(LLM_KV_EXPERT_GATING_FUNC,         hparams.expert_gating_func);
+    ml.get_key(LLM_KV_EXPERT_LATENT_LENGTH,       hparams.n_expert_latent, false);
+
+    ml.get_key(LLM_KV_ATTN_RES_BLOCK_SIZE,          hparams.attn_res_block_size);
+    ml.get_key(LLM_KV_ACTIVATION_SITU_BETA,         hparams.situ_beta);
+    ml.get_key(LLM_KV_ACTIVATION_SITU_LINEAR_BETA,  hparams.situ_linear_beta);
+
+    switch (hparams.n_layer()) {
+        case 93: type = LLM_TYPE_2_8T_A50B; break; // Kimi-K3
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_kimi_k3::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    const int64_t n_embd_latent = hparams.n_expert_latent > 0 ? hparams.n_expert_latent : n_embd;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
+
+    if (hparams.attn_res_block_size > 0) {
+        output_res_score = create_tensor(tn(LLM_TENSOR_OUTPUT_RES_SCORE, "weight"), {n_embd}, 0);
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+        layer.ffn_norm  = create_tensor(tn(LLM_TENSOR_FFN_NORM,  "weight", i), {n_embd}, 0);
+
+        if (hparams.attn_res_block_size > 0) {
+            layer.attn_res_score = create_tensor(tn(LLM_TENSOR_ATTN_RES_SCORE, "weight", i), {n_embd}, 0);
+            layer.ffn_res_score  = create_tensor(tn(LLM_TENSOR_FFN_RES_SCORE,  "weight", i), {n_embd}, 0);
+        }
+
+        const int64_t head_dim = hparams.n_embd_head_kda;
+        const int64_t d_conv   = hparams.ssm_d_conv;
+        const int64_t d_inner  = head_dim * n_head;
+
+        if (hparams.is_recr(i)) {
+            // conv1d may be stored 4D [d_conv, 1, d_inner, 1] or 3D (quantization drops the trailing 1)
+            auto conv = [&](llm_tensor tid) {
+                ggml_tensor * t = create_tensor(tn(tid, "weight", i), {d_conv, 1, d_inner, 1}, TENSOR_NOT_REQUIRED);
+                return t ? t : create_tensor(tn(tid, "weight", i), {d_conv, 1, d_inner}, 0);
+            };
+            layer.ssm_q_conv = conv(LLM_TENSOR_SSM_CONV1D_Q);
+            layer.ssm_k_conv = conv(LLM_TENSOR_SSM_CONV1D_K);
+            layer.ssm_v_conv = conv(LLM_TENSOR_SSM_CONV1D_V);
+
+            create_tensor_qkv(layer, i, n_embd, d_inner, d_inner, d_inner, 0);
+
+            layer.ssm_f_a  = create_tensor(tn(LLM_TENSOR_SSM_F_A,  "weight", i), {n_embd, head_dim}, 0);
+            layer.ssm_f_b  = create_tensor(tn(LLM_TENSOR_SSM_F_B,  "weight", i), {head_dim, d_inner}, 0);
+            layer.ssm_beta = create_tensor(tn(LLM_TENSOR_SSM_BETA, "weight", i), {n_embd, n_head}, 0);
+
+            // K3's A_log is a plain 1-D [n_head] tensor (kimi-linear's is padded)
+            layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A, i), {n_head}, 0);
+            layer.ssm_dt_b = create_tensor(tn(LLM_TENSOR_SSM_DT, "bias", i), {d_inner}, 0);
+
+            // K3 uses a single full-rank gate instead of kimi-linear's g_a/g_b pair
+            layer.ssm_g      = create_tensor(tn(LLM_TENSOR_SSM_G,    "weight", i), {n_embd, d_inner}, 0);
+            layer.ssm_o_norm = create_tensor(tn(LLM_TENSOR_SSM_NORM, "weight", i), {head_dim}, 0);
+            layer.wo         = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {d_inner, n_embd}, 0);
+        } else {
+            const int64_t q_lora_rank      = hparams.n_lora_q;
+            const int64_t kv_lora_rank     = hparams.n_lora_kv;
+            const int64_t n_embd_head_k    = hparams.n_embd_head_k_mla();
+            const int64_t n_embd_head_v    = hparams.n_embd_head_v_mla();
+            const int64_t qk_rope_head_dim = hparams.n_rot();
+            const int64_t qk_nope_head_dim = n_embd_head_k - qk_rope_head_dim;
+
+            layer.attn_q_a_norm  = create_tensor(tn(LLM_TENSOR_ATTN_Q_A_NORM,  "weight", i), {q_lora_rank}, TENSOR_NOT_REQUIRED);
+            layer.attn_kv_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_NORM, "weight", i), {kv_lora_rank}, 0);
+
+            if (layer.attn_q_a_norm) {
+                layer.wq_a = create_tensor(tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank}, 0);
+                layer.wq_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, n_head * n_embd_head_k}, 0);
+            } else {
+                layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_head * n_embd_head_k}, 0);
+            }
+
+            layer.wkv_a_mqa = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i), {n_embd, kv_lora_rank + qk_rope_head_dim}, 0);
+            layer.wkv_b     = create_tensor(tn(LLM_TENSOR_ATTN_KV_B, "weight", i),
+                                            {kv_lora_rank, n_head * (qk_nope_head_dim + n_embd_head_v)},
+                                            TENSOR_NOT_REQUIRED | TENSOR_SKIP_IF_VIRTUAL);
+            if (!layer.wkv_b) {
+                layer.wk_b = create_tensor(tn(LLM_TENSOR_ATTN_K_B, "weight", i), {qk_nope_head_dim, kv_lora_rank, n_head}, 0);
+                layer.wv_b = create_tensor(tn(LLM_TENSOR_ATTN_V_B, "weight", i), {kv_lora_rank, n_embd_head_v, n_head}, 0);
+            }
+
+            // K3: sigmoid output gate applied to the attention output before o_proj
+            layer.wqkv_gate = create_tensor(tn(LLM_TENSOR_ATTN_GATE, "weight", i), {n_embd, n_head * n_embd_head_v}, TENSOR_NOT_REQUIRED);
+
+            layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_head * n_embd_head_v, n_embd}, 0);
+        }
+
+        if (i < (int) hparams.n_layer_dense_lead) {
+            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
+            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
+            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, 0);
+        } else {
+            const int64_t n_ff_exp = hparams.n_ff_exp;
+
+            layer.ffn_gate_inp    = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,    "weight", i), {n_embd, n_expert}, 0);
+            layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias",   i), {n_expert}, 0);
+
+            // routed experts live in the latent space
+            layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd_latent, n_ff_exp, n_expert}, 0);
+            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd_latent, n_expert}, 0);
+            layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd_latent, n_ff_exp, n_expert}, 0);
+
+            if (hparams.n_expert_latent > 0) {
+                layer.ffn_routed_down = create_tensor(tn(LLM_TENSOR_FFN_ROUTED_DOWN, "weight", i), {n_embd, n_embd_latent}, 0);
+                layer.ffn_routed_up   = create_tensor(tn(LLM_TENSOR_FFN_ROUTED_UP,   "weight", i), {n_embd_latent, n_embd}, 0);
+                layer.ffn_routed_norm = create_tensor(tn(LLM_TENSOR_FFN_ROUTED_NORM, "weight", i), {n_embd_latent}, TENSOR_NOT_REQUIRED);
+            }
+
+            // shared experts stay at n_embd, width = moe_intermediate_size * n_expert_shared
+            const int64_t n_ff_shexp = n_ff_exp * (hparams.n_expert_shared > 0 ? hparams.n_expert_shared : 1);
+            layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_shexp}, TENSOR_NOT_REQUIRED);
+            layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp, n_embd}, TENSOR_NOT_REQUIRED);
+            layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, n_ff_shexp}, TENSOR_NOT_REQUIRED);
+        }
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_kimi_k3::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+// situ(gate, up) = beta*tanh(gate/beta)*sigmoid(gate) * linear_beta*tanh(up/linear_beta)
+// linear_beta <= 0 disables the transform on the up branch
+static ggml_tensor * kimi_k3_situ(ggml_context * ctx0, ggml_tensor * gate, ggml_tensor * up,
+                                  float beta, float linear_beta) {
+    ggml_tensor * a = ggml_scale(ctx0, ggml_tanh(ctx0, ggml_scale(ctx0, gate, 1.0f/beta)), beta);
+    a = ggml_mul(ctx0, a, ggml_sigmoid(ctx0, gate));
+
+    if (linear_beta > 0.0f) {
+        up = ggml_scale(ctx0, ggml_tanh(ctx0, ggml_scale(ctx0, up, 1.0f/linear_beta)), linear_beta);
+    }
+    return ggml_mul(ctx0, a, up);
+}
+
+//
+// cross-layer residual attention
+//
+
+// layout is [n_embd, n_ckpt, n_tokens]: rms_norm reduces over ne0, dsv4_hc_pre over ne1
+// append the new checkpoint, do not re-fold the whole chain
+void llama_model_kimi_k3::graph::res_push(ggml_tensor * cur, int64_t n_embd, int64_t n_tokens) {
+    ggml_tensor * ckpt = ggml_reshape_3d(ctx0, cur, n_embd, 1, n_tokens);
+
+    resi_stack = resi_stack ? ggml_concat(ctx0, resi_stack, ckpt, 1) : ckpt;
+}
+
+ggml_tensor * llama_model_kimi_k3::graph::res_mix(ggml_tensor * cur, ggml_tensor * score_w,
+                                                  int64_t n_tokens, int il) {
+    if (!resi_stack) {
+        return cur; // layer 0: nothing banked yet
+    }
+
+    const int   n_ckpt = (int) resi_stack->ne[1];
+    const float eps    = hparams.f_norm_rms_eps;
+
+    ggml_tensor * src = resi_stack;   // [n_embd, n_ckpt, n_tokens]
+
+    // one rms_norm scores all checkpoints at once
+    // note: the scores use the normalized values, but the sum below uses the raw ones
+    ggml_tensor * sc_src = ggml_rms_norm(ctx0, src, eps);
+    sc_src = ggml_mul(ctx0, sc_src, score_w);
+    sc_src = ggml_sum_rows(ctx0, sc_src);                          // [1, n_ckpt, n_tokens]
+    sc_src = ggml_reshape_2d(ctx0, sc_src, n_ckpt, n_tokens);
+
+    // the current residual stream is scored apart, so the stack stays append-only
+    ggml_tensor * sc_cur = ggml_rms_norm(ctx0, cur, eps);
+    sc_cur = ggml_mul(ctx0, sc_cur, score_w);
+    sc_cur = ggml_sum_rows(ctx0, sc_cur);                          // [1, n_tokens]
+
+    ggml_tensor * scores = ggml_concat(ctx0, sc_src, sc_cur, 0);   // [n_ckpt+1, n_tokens]
+    ggml_tensor * probs  = ggml_soft_max(ctx0, scores);            // over ne0 = n_ckpt+1
+    cb(probs, "res_probs", il);
+
+    // split the sum: hc_pre handles the stack, a broadcast-multiply the current stream
+    ggml_tensor * p_src = ggml_cont(ctx0, ggml_view_2d(ctx0, probs, n_ckpt, n_tokens, probs->nb[1], 0));
+    ggml_tensor * p_cur = ggml_cont(ctx0, ggml_view_2d(ctx0, probs, 1, n_tokens, probs->nb[1],
+                                                       probs->nb[0] * n_ckpt));
+
+    ggml_tensor * out = ggml_dsv4_hc_pre(ctx0, src, p_src);
+    out = ggml_add(ctx0, out, ggml_mul(ctx0, cur, p_cur));
+
+    return out;
+}
+
+llama_model_kimi_k3::graph::graph(const llama_model & model, const llm_graph_params & params) :
+    llm_build_delta_net_base(params), model(model) {
+
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+    cb(inpL, "inp_embd", -1);
+
+    // K3 MLA is nope-only, so there is no position input
+
+    auto * inp_kv      = !hparams.is_mla() ? build_inp_mem_hybrid()   : nullptr;
+    auto * inp_k       =  hparams.is_mla() ? build_inp_mem_hybrid_k() : nullptr;
+    auto * inp_rs      =  hparams.is_mla() ? inp_k->get_recr() : inp_kv->get_recr();
+    auto * inp_attn_kv = !hparams.is_mla() ? inp_kv->get_attn() : nullptr;
+    auto * inp_attn_k  =  hparams.is_mla() ? inp_k->get_attn()  : nullptr;
+
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    const int64_t n_head_kda = hparams.n_head();
+    const int64_t head_dim   = hparams.n_embd_head_kda;
+    const int64_t d_conv     = hparams.ssm_d_conv;
+    const int64_t d_inner    = n_head_kda * head_dim;
+    const int64_t n_seqs     = ubatch.n_seqs;
+    const int64_t n_seq_tokens = ubatch.n_seq_tokens;
+
+    GGML_ASSERT(n_seqs != 0);
+    GGML_ASSERT(ubatch.equal_seqs());
+    GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs);
+
+    const int64_t n_embd_head_k_mla   = hparams.n_embd_head_k_mla();
+    const int64_t n_embd_head_v_mla   = hparams.n_embd_head_v_mla();
+    const int64_t kv_lora_rank        = hparams.n_lora_kv;
+    const int64_t n_embd_head_qk_rope = hparams.n_rot();
+    const int64_t n_embd_head_qk_nope = n_embd_head_k_mla - n_embd_head_qk_rope;
+    const float   kq_scale_mla        = 1.0f / sqrtf((float) n_embd_head_k_mla);
+
+    const uint32_t res_bs        = hparams.attn_res_block_size;
+    const bool     use_attn_res  = res_bs > 0;
+    const int64_t  n_embd_latent = hparams.n_expert_latent > 0 ? hparams.n_expert_latent : n_embd;
+
+    for (int il = 0; il < n_layer; ++il) {
+        const auto & layer = model.layers[il];
+
+        // the residual stream, banked on checkpoint layers and then restarted
+        // from the attention output alone
+        ggml_tensor * prefix_sum = inpL;
+
+        cur = use_attn_res ? res_mix(prefix_sum, layer.attn_res_score, n_tokens, il)
+                           : prefix_sum;
+
+        bool banked = false;
+        if (use_attn_res && (uint32_t) il % res_bs == 0) {
+            res_push(prefix_sum, n_embd, n_tokens);  // banks the RAW layer input, not `cur`
+            banked = true;
+        }
+
+        cur = build_norm(cur, layer.attn_norm, NULL, LLM_NORM_RMS, il);
+        cb(cur, "attn_norm", il);
+        ggml_build_forward_expand(gf, cur);
+
+        if (hparams.is_recr(il)) {
+            cur = build_kda_layer(cur, layer, inp_rs, d_conv, head_dim, n_head_kda,
+                                  d_inner, n_seq_tokens, n_seqs, il);
+        } else {
+            cur = build_mla_layer(cur, layer, inp_attn_k, inp_attn_kv,
+                                  n_embd_head_k_mla, n_embd_head_v_mla, kv_lora_rank,
+                                  n_embd_head_qk_rope, n_embd_head_qk_nope, kq_scale_mla, il);
+        }
+
+        prefix_sum = banked ? cur : ggml_add(ctx0, prefix_sum, cur);
+        cb(prefix_sum, "prefix_sum_attn", il);
+
+        cur = use_attn_res ? res_mix(prefix_sum, layer.ffn_res_score, n_tokens, il)
+                           : prefix_sum;
+
+        cur = build_norm(cur, layer.ffn_norm, NULL, LLM_NORM_RMS, il);
+        cb(cur, "ffn_norm", il);
+
+        if ((uint32_t) il < hparams.n_layer_dense_lead) {
+            ggml_tensor * g = ggml_mul_mat(ctx0, layer.ffn_gate, cur);
+            ggml_tensor * u = ggml_mul_mat(ctx0, layer.ffn_up,   cur);
+            cur = kimi_k3_situ(ctx0, g, u, hparams.situ_beta, hparams.situ_linear_beta);
+            cur = ggml_mul_mat(ctx0, layer.ffn_down, cur);
+            cb(cur, "ffn_out", il);
+        } else {
+            cur = build_latent_moe(cur, layer, n_embd_latent, il);
+        }
+
+        prefix_sum = ggml_add(ctx0, prefix_sum, cur);
+        prefix_sum = build_cvec(prefix_sum, il);
+        cb(prefix_sum, "l_out", il);
+
+        inpL = prefix_sum;
+    }
+
+    cur = inpL;
+
+    // final mix, then narrow to the output tokens
+    if (use_attn_res) {
+        cur = res_mix(cur, model.output_res_score, n_tokens, -1);
+    }
+    if (inp_out_ids) {
+        cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+    }
+
+    cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    cur = ggml_mul_mat(ctx0, model.output, cur);
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
+
+//
+// KDA layer
+//
+
+// causal conv1d over one of Q/K/V. `qkv` selects which third of the conv state to use
+static ggml_tensor * kimi_k3_conv1d(ggml_cgraph * gf, ggml_context * ctx0,
+                                    ggml_tensor * conv_states_all, ggml_tensor * conv_state_all,
+                                    int64_t qkv, ggml_tensor * x, ggml_tensor * proj_w, ggml_tensor * conv_w,
+                                    int64_t d_conv, int64_t head_dim, int64_t n_head,
+                                    int64_t n_seq_tokens, int64_t n_seqs, int64_t n_tokens, int64_t kv_head) {
+    const int64_t d_inner         = head_dim * n_head;
+    const int64_t conv_state_size = (d_conv - 1) * d_inner;
+    const int64_t n_embd_r_total  = 3 * conv_state_size;
+
+    ggml_tensor * conv_state_x = ggml_view_3d(ctx0, conv_state_all, d_conv - 1, d_inner, n_seqs,
+        (d_conv - 1)   * ggml_element_size(conv_state_all),
+        n_embd_r_total * ggml_element_size(conv_state_all),
+        qkv * conv_state_size * ggml_element_size(conv_state_all));
+
+    ggml_tensor * x_proj = ggml_mul_mat(ctx0, proj_w, x);
+    ggml_tensor * x_3d   = ggml_reshape_3d(ctx0, x_proj, d_inner, n_seq_tokens, n_seqs);
+    ggml_tensor * conv_x = ggml_concat(ctx0, conv_state_x, ggml_transpose(ctx0, x_3d), 0);
+
+    ggml_tensor * last_conv_x = ggml_view_3d(ctx0, conv_x, d_conv - 1, d_inner, n_seqs,
+        conv_x->nb[1], conv_x->nb[2], n_seq_tokens * conv_x->nb[0]);
+    ggml_build_forward_expand(gf,
+        ggml_cpy(ctx0, last_conv_x,
+            ggml_view_3d(ctx0, conv_states_all, d_conv - 1, d_inner, n_seqs,
+                (d_conv - 1)   * ggml_element_size(conv_states_all),
+                n_embd_r_total * ggml_element_size(conv_states_all),
+                (kv_head * n_embd_r_total + qkv * conv_state_size) * ggml_element_size(conv_states_all))));
+
+    ggml_tensor * conv_weight = ggml_reshape_2d(ctx0, conv_w, d_conv, d_inner);
+    ggml_tensor * Xcur = ggml_ssm_conv(ctx0, conv_x, conv_weight);
+    Xcur = ggml_reshape_2d(ctx0, Xcur, d_inner, n_tokens);
+    Xcur = ggml_silu(ctx0, Xcur);
+
+    return ggml_reshape_4d(ctx0, Xcur, head_dim, n_head, n_seq_tokens, n_seqs);
+}
+
+ggml_tensor * llama_model_kimi_k3::graph::build_kda_layer(
+        ggml_tensor * cur, const llama_layer & layer, llm_graph_input_rs * inp_rs,
+        int64_t d_conv, int64_t head_dim, int64_t n_head_kda,
+        int64_t d_inner, int64_t n_seq_tokens, int64_t n_seqs, int il) {
+
+    const auto * mctx_cur = inp_rs->mctx;
+    const auto   kv_head  = mctx_cur->get_head();
+
+    ggml_tensor * conv_states_all = mctx_cur->get_r_l(il);
+    ggml_tensor * conv_state_all  = build_rs(inp_rs, conv_states_all, hparams.n_embd_r(), n_seqs);
+
+    ggml_tensor * Qcur = kimi_k3_conv1d(gf, ctx0, conv_states_all, conv_state_all, 0, cur, layer.wq, layer.ssm_q_conv, d_conv, head_dim, n_head_kda, n_seq_tokens, n_seqs, n_tokens, kv_head);
+    ggml_tensor * Kcur = kimi_k3_conv1d(gf, ctx0, conv_states_all, conv_state_all, 1, cur, layer.wk, layer.ssm_k_conv, d_conv, head_dim, n_head_kda, n_seq_tokens, n_seqs, n_tokens, kv_head);
+    ggml_tensor * Vcur = kimi_k3_conv1d(gf, ctx0, conv_states_all, conv_state_all, 2, cur, layer.wv, layer.ssm_v_conv, d_conv, head_dim, n_head_kda, n_seq_tokens, n_seqs, n_tokens, kv_head);
+    cb(Qcur, "kda_q_conv", il);
+    cb(Kcur, "kda_k_conv", il);
+    cb(Vcur, "kda_v_conv", il);
+
+    // gate_lower_bound is not a clamp - when set, it swaps the decay gate activation:
+    //   unset (kimi-linear):  g = -exp(A_log) * softplus(f_b(f_a(x)) + dt_bias)
+    //   set   (K3, -5.0):     g = lower_bound * sigmoid(exp(A_log) * (f_b(f_a(x)) + dt_bias))
+    // ssm_a holds -exp(A_log) (folded at conversion time), so exp(A_log) == -ssm_a
+    ggml_tensor * f_a = ggml_mul_mat(ctx0, layer.ssm_f_a, cur);
+    ggml_tensor * g1  = ggml_mul_mat(ctx0, layer.ssm_f_b, f_a);
+    g1 = ggml_add(ctx0, g1, layer.ssm_dt_b);
+
+    ggml_tensor * A = ggml_reshape_3d(ctx0, layer.ssm_a, 1, n_head_kda, 1);
+
+    if (hparams.kda_gate_lower_bound > -INFINITY) {
+        g1 = ggml_reshape_3d(ctx0, g1, head_dim, n_head_kda, n_tokens);
+        g1 = ggml_mul(ctx0, g1, A);                                    // -exp(A_log) * (...)
+        g1 = ggml_sigmoid(ctx0, ggml_scale(ctx0, g1, -1.0f));
+        g1 = ggml_scale(ctx0, g1, hparams.kda_gate_lower_bound);
+    } else {
+        g1 = ggml_softplus(ctx0, g1);
+        g1 = ggml_reshape_3d(ctx0, g1, head_dim, n_head_kda, n_tokens);
+        g1 = ggml_mul(ctx0, g1, A);
+    }
+    cb(g1, "kda_g1", il);
+
+    g1 = ggml_reshape_4d(ctx0, g1, head_dim, n_head_kda, n_seq_tokens, n_seqs);
+
+    ggml_tensor * beta = ggml_mul_mat(ctx0, layer.ssm_beta, cur);
+    beta = ggml_reshape_4d(ctx0, beta, 1, n_head_kda, n_seq_tokens, n_seqs);
+    beta = ggml_sigmoid(ctx0, beta);
+    cb(beta, "kda_beta", il);
+
+    ggml_tensor * cur_3d = ggml_reshape_3d(ctx0, cur, cur->ne[0], n_seq_tokens, n_seqs);
+
+    ggml_tensor * ssm_states_all = mctx_cur->get_s_l(il);
+    ggml_tensor * state = build_rs(inp_rs, ssm_states_all, hparams.n_embd_s(), n_seqs);
+    state = ggml_reshape_4d(ctx0, state, head_dim, head_dim, n_head_kda, n_seqs);
+
+    const float eps = hparams.f_norm_rms_eps;
+    Qcur = ggml_l2_norm(ctx0, Qcur, eps);
+    Kcur = ggml_l2_norm(ctx0, Kcur, eps);
+
+    auto attn_out = build_delta_net(Qcur, Kcur, Vcur, g1, beta, state, il);
+
+    ggml_tensor * output    = ggml_cont(ctx0, attn_out.first);
+    cb(output, "kda_scan_out", il);
+    ggml_tensor * new_state = attn_out.second;
+
+    ggml_build_forward_expand(gf,
+        ggml_cpy(ctx0, new_state,
+            ggml_view_1d(ctx0, ssm_states_all, hparams.n_embd_s() * n_seqs,
+                         kv_head * hparams.n_embd_s() * ggml_element_size(ssm_states_all))));
+
+    // K3: single full-rank gate (kimi-linear factors this as g_b(g_a(x)))
+    ggml_tensor * cur_2d = ggml_reshape_2d(ctx0, cur_3d, cur_3d->ne[0], n_seq_tokens * n_seqs);
+    ggml_tensor * g2     = ggml_mul_mat(ctx0, layer.ssm_g, cur_2d);
+    g2 = ggml_reshape_3d(ctx0, g2, head_dim, n_head_kda, n_seq_tokens * n_seqs);
+
+    ggml_tensor * o      = ggml_reshape_3d(ctx0, output, head_dim, n_head_kda, n_seq_tokens * n_seqs);
+    ggml_tensor * normed = build_norm(o, layer.ssm_o_norm, nullptr, LLM_NORM_RMS, il);
+    cb(g2, "kda_g2", il);
+    cb(normed, "kda_normed", il);
+    ggml_tensor * gated  = ggml_mul(ctx0, normed, ggml_sigmoid(ctx0, g2));
+
+    gated = ggml_cont_2d(ctx0, gated, d_inner, n_tokens);
+    cur   = ggml_mul_mat(ctx0, layer.wo, gated);
+    cb(cur, "kda_out", il);
+
+    return cur;
+}
+
+//
+// MLA layer (nope-only, with K3's sigmoid output gate)
+//
+
+ggml_tensor * llama_model_kimi_k3::graph::build_mla_layer(
+        ggml_tensor * cur, const llama_layer & layer,
+        llm_graph_input_attn_k * inp_attn_k, llm_graph_input_attn_kv * inp_attn_kv,
+        int64_t n_embd_head_k_mla, int64_t n_embd_head_v_mla, int64_t kv_lora_rank,
+        int64_t n_embd_head_qk_rope, int64_t n_embd_head_qk_nope, float kq_scale, int il) {
+
+    ggml_tensor * inp_gate = cur; // the output gate reads the *normed* layer input
+
+    ggml_tensor * Qcur;
+    if (layer.wq_a) {
+        Qcur = ggml_mul_mat(ctx0, layer.wq_a, cur);
+        Qcur = build_norm(Qcur, layer.attn_q_a_norm, nullptr, LLM_NORM_RMS, il);
+        Qcur = ggml_mul_mat(ctx0, layer.wq_b, Qcur);
+    } else {
+        Qcur = ggml_mul_mat(ctx0, layer.wq, cur);
+    }
+
+    ggml_tensor * kv_cmpr_pe = ggml_mul_mat(ctx0, layer.wkv_a_mqa, cur);
+
+    ggml_tensor * kv_cmpr = ggml_view_2d(ctx0, kv_cmpr_pe, kv_lora_rank, n_tokens,
+        ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope), 0);
+    ggml_tensor * k_pe = ggml_view_3d(ctx0, kv_cmpr_pe, n_embd_head_qk_rope, 1, n_tokens,
+        ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope),
+        ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope),
+        ggml_row_size(kv_cmpr_pe->type, kv_lora_rank));
+
+    // no RoPE: mla_use_nope is asserted at conversion time
+    kv_cmpr = build_norm(kv_cmpr, layer.attn_kv_a_norm, nullptr, LLM_NORM_RMS, il);
+
+    ggml_tensor * out;
+    if (layer.wk_b && layer.wv_b) {
+        ggml_tensor * q_nope = ggml_view_3d(ctx0, Qcur, n_embd_head_qk_nope, n_head, n_tokens,
+            ggml_row_size(Qcur->type, n_embd_head_k_mla),
+            ggml_row_size(Qcur->type, n_embd_head_k_mla) * n_head, 0);
+        ggml_tensor * q_pe = ggml_view_3d(ctx0, Qcur, n_embd_head_qk_rope, n_head, n_tokens,
+            ggml_row_size(Qcur->type, n_embd_head_k_mla),
+            ggml_row_size(Qcur->type, n_embd_head_k_mla) * n_head,
+            ggml_row_size(Qcur->type, n_embd_head_qk_nope));
+
+        q_nope = ggml_permute(ctx0, q_nope, 0, 2, 1, 3);
+        ggml_tensor * q_nope_absorbed = ggml_mul_mat(ctx0, layer.wk_b, q_nope);
+        q_nope_absorbed = ggml_permute(ctx0, q_nope_absorbed, 0, 2, 1, 3);
+
+        ggml_tensor * Q = ggml_concat(ctx0, q_nope_absorbed, q_pe, 0);
+        ggml_tensor * kv_cmpr_3d = ggml_reshape_3d(ctx0, kv_cmpr, kv_lora_rank, 1, n_tokens);
+        ggml_tensor * K = ggml_concat(ctx0, kv_cmpr_3d, k_pe, 0);
+        ggml_tensor * V = kv_cmpr_3d;
+
+        // wo == NULL: the output projection is applied after the gate below
+        out = build_attn(inp_attn_k, nullptr, NULL, nullptr, Q, K, V, nullptr, nullptr, layer.wv_b, kq_scale, il);
+    } else {
+        ggml_tensor * Q = ggml_reshape_3d(ctx0, Qcur, n_embd_head_k_mla, n_head, n_tokens);
+        ggml_tensor * kv = ggml_mul_mat(ctx0, layer.wkv_b, kv_cmpr);
+        const int64_t kv_per_head = n_embd_head_qk_nope + n_embd_head_v_mla;
+
+        ggml_tensor * k_nope = ggml_view_3d(ctx0, kv, n_embd_head_qk_nope, n_head, n_tokens,
+            ggml_row_size(kv->type, kv_per_head), ggml_row_size(kv->type, kv_per_head * n_head), 0);
+        ggml_tensor * V = ggml_cont(ctx0, ggml_view_3d(ctx0, kv, n_embd_head_v_mla, n_head, n_tokens,
+            ggml_row_size(kv->type, kv_per_head), ggml_row_size(kv->type, kv_per_head * n_head),
+            ggml_row_size(kv->type, n_embd_head_qk_nope)));
+
+        ggml_tensor * k_pe_t = ggml_new_tensor_3d(ctx0, k_pe->type, n_embd_head_qk_rope, n_head, n_tokens);
+        ggml_tensor * K = ggml_concat(ctx0, ggml_repeat(ctx0, k_pe, k_pe_t), k_nope, 0);
+
+        out = build_attn(inp_attn_kv, nullptr, NULL, nullptr, Q, K, V, nullptr, nullptr, nullptr, kq_scale, il);
+    }
+
+    // K3: attn_output *= sigmoid(g_proj(x)), then o_proj
+    if (layer.wqkv_gate) {
+        ggml_tensor * g = ggml_sigmoid(ctx0, ggml_mul_mat(ctx0, layer.wqkv_gate, inp_gate));
+        out = ggml_mul(ctx0, out, g);
+        cb(out, "mla_gated", il);
+    }
+
+    out = ggml_mul_mat(ctx0, layer.wo, out);
+    cb(out, "mla_out", il);
+
+    return out;
+}
+
+//
+// latent MoE: down-project, run the routed experts in the latent space, norm, up-project;
+// shared experts stay at n_embd and read the un-projected input.
+//
+
+ggml_tensor * llama_model_kimi_k3::graph::build_latent_moe(
+        ggml_tensor * cur, const llama_layer & layer, int64_t n_embd_latent, int il) {
+
+    ggml_tensor * identity = cur;
+
+    ggml_tensor * routed_in = layer.ffn_routed_down
+        ? ggml_mul_mat(ctx0, layer.ffn_routed_down, cur)
+        : cur;
+
+    // the router scores the full-width input while the experts take the latent one,
+    // so the logits are computed here and passed to build_moe_ffn
+    ggml_tensor * logits = ggml_mul_mat(ctx0, layer.ffn_gate_inp, identity);
+    cb(logits, "ffn_moe_logits", il);
+
+    ggml_tensor * moe_out = build_moe_ffn(routed_in,
+        nullptr, // gate_inp unused: the logits above are passed instead
+        layer.ffn_up_exps,
+        layer.ffn_gate_exps,
+        layer.ffn_down_exps,
+        layer.ffn_exp_probs_b,
+        hparams.n_expert,
+        hparams.n_expert_used,
+        LLM_FFN_SITU, hparams.expert_weights_norm,
+        hparams.expert_weights_scale,
+        (llama_expert_gating_func_type) hparams.expert_gating_func,
+        il,
+        logits);
+    cb(moe_out, "ffn_moe_out", il);
+
+    if (layer.ffn_routed_norm) {
+        moe_out = build_norm(moe_out, layer.ffn_routed_norm, NULL, LLM_NORM_RMS, il);
+    }
+    if (layer.ffn_routed_up) {
+        moe_out = ggml_mul_mat(ctx0, layer.ffn_routed_up, moe_out);
+    }
+    GGML_UNUSED(n_embd_latent);
+
+    if (layer.ffn_gate_shexp) {
+        ggml_tensor * g = ggml_mul_mat(ctx0, layer.ffn_gate_shexp, identity);
+        ggml_tensor * u = ggml_mul_mat(ctx0, layer.ffn_up_shexp,   identity);
+        ggml_tensor * sh = kimi_k3_situ(ctx0, g, u, hparams.situ_beta, hparams.situ_linear_beta);
+        sh = ggml_mul_mat(ctx0, layer.ffn_down_shexp, sh);
+        cb(sh, "ffn_shexp", il);
+        moe_out = ggml_add(ctx0, moe_out, sh);
+    }
+
+    cb(moe_out, "ffn_out", il);
+    return moe_out;
+}

src/models/minimax-01.cpp

@@ -0,0 +1,520 @@
+#include "models.h"
+#include "llama-memory-recurrent.h"
+
+void llama_model_minimax_01::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    ml.get_key(LLM_KV_RESIDUAL_SCALE,              hparams.f_residual_scale);
+
+    // we use n_embd_head_la to set recurrent memory n_embd_s
+    hparams.n_embd_head_la = hparams.n_embd_head_k_full;
+
+    // Mark recurrent layers (lightning attention layers).
+    if (!ml.get_key_or_arr(LLM_KV_ATTENTION_RECURRENT_LAYERS, hparams.is_recr_impl, hparams.n_layer_all, false)) {
+        uint32_t full_attn_interval = 8;
+        ml.get_key(LLM_KV_FULL_ATTENTION_INTERVAL, full_attn_interval, false);
+        for (uint32_t i = 0; i < hparams.n_layer_all; ++i) {
+            hparams.is_recr_impl[i] = (i < hparams.n_layer()) && ((i + 1) % full_attn_interval != 0);
+        }
+    }
+
+    switch (hparams.n_layer()) {
+        case 80: type = LLM_TYPE_456B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_minimax_01::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+
+    // if output is NULL, init from the input tok embed
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        if (!hparams.is_recr(i)) {
+            create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);
+        } else {
+            layer.attn_norm_2 = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd_head_k * n_head}, 0);
+            layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, 3 * n_embd_head_k * n_head}, 0);
+            layer.wg = create_tensor(tn(LLM_TENSOR_ATTN_GATE, "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
+        }
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+        layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {n_embd, n_expert}, 0);
+        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd,   n_ff, n_expert}, TENSOR_NOT_REQUIRED);
+        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {  n_ff, n_embd, n_expert}, 0);
+        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd,   n_ff, n_expert}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_minimax_01::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+class llm_graph_input_la : public llm_graph_input_i {
+public:
+    llm_graph_input_la(const llama_hparams & hparams) : hparams(hparams) {}
+
+    void set_input(const llama_ubatch * ubatch) override {
+        // this operates on assumption that we have an equal ubatch split
+
+        const int64_t n_head = hparams.n_head();
+        const int32_t n_seqs = ubatch->n_seqs;
+        const int32_t n_seqs_unq = ubatch->n_seqs_unq;
+        const int32_t n_tokens = ubatch->n_tokens;
+        const int32_t n_seq_tokens = ubatch->n_seq_tokens;
+
+        std::vector<llama_pos> p0(n_seqs_unq);
+        std::fill(p0.begin(), p0.end(), std::numeric_limits<llama_pos>::max());
+
+        // get lowest token position in a ubatch for each stream
+        for (int i = 0; i < n_tokens; ++i) {
+            llama_seq_id seq_id = ubatch->seq_id[i][0];
+            int32_t seq_idx = ubatch->seq_idx[seq_id];
+            llama_pos pos = ubatch->pos[i];
+            if (p0[seq_idx] > pos) {
+                p0[seq_idx] = pos;
+            }
+        }
+
+        if (inp_slopes) {
+            GGML_ASSERT(ggml_backend_buffer_is_host(inp_slopes->buffer));
+
+            float * data = (float *) inp_slopes->data;
+
+            float start = powf(2, -powf(2, -(log2f(n_head) - 3)));
+            float ratio = start;
+
+            for (int h = 0; h < n_head; ++h) {
+                data[h] = start * powf(ratio, h);
+            }
+        }
+
+        if (inp_q_decay) {
+            GGML_ASSERT(ggml_backend_buffer_is_host(inp_q_decay->buffer));
+
+            float * slopes = (float *) inp_slopes->data;
+            float * data = (float *) inp_q_decay->data;
+
+            for (int s = 0; s < n_seqs; ++s) {
+                for (int i = 0; i < n_seq_tokens; ++i) {
+                    llama_seq_id seq_id = ubatch->seq_id[s * n_seq_tokens + i][0];
+                    int32_t seq_idx = ubatch->seq_idx[seq_id];
+                    llama_pos pos = ubatch->pos[s * n_seq_tokens + i];
+                    int pos_rel = pos - p0[seq_idx];
+
+                    for (int h = 0; h < n_head; ++h) {
+                        data[seq_idx * n_head * n_seq_tokens + i * n_head + h] = -slopes[h] * (pos_rel + 1);
+                    }
+                }
+            }
+        }
+
+        if (inp_k_decay) {
+            GGML_ASSERT(ggml_backend_buffer_is_host(inp_k_decay->buffer));
+
+            float * slopes = (float *) inp_slopes->data;
+            float * data = (float *) inp_k_decay->data;
+
+            for (int s = 0; s < n_seqs; ++s) {
+                for (int i = 0; i < n_seq_tokens; ++i) {
+                    llama_seq_id seq_id = ubatch->seq_id[s * n_seq_tokens + i][0];
+                    int32_t seq_idx = ubatch->seq_idx[seq_id];
+                    llama_pos pos = ubatch->pos[s * n_seq_tokens + i];
+                    int pos_rel = pos - p0[seq_idx];
+
+                    for (int h = 0; h < n_head; ++h) {
+                        data[seq_idx * n_head * n_seq_tokens + i * n_head + h] = -slopes[h] * (n_seq_tokens - pos_rel - 1);
+                    }
+                }
+            }
+        }
+
+        if (inp_diag_decay) {
+            GGML_ASSERT(ggml_backend_buffer_is_host(inp_diag_decay->buffer));
+
+            float * slopes = (float *) inp_slopes->data;
+            float * data = (float *) inp_diag_decay->data;
+
+            for (int s = 0; s < n_seqs; ++s) {
+                for (int h = 0; h < n_head; ++h) {
+                    for (int j = 0; j < n_seq_tokens; ++j) {
+                        llama_seq_id seq_id = ubatch->seq_id[s * n_seq_tokens + j][0];
+                        int32_t seq_idx = ubatch->seq_idx[seq_id];
+                        llama_pos pos_j = ubatch->pos[s * n_seq_tokens + j];
+                        int pos_rel_j = pos_j - p0[seq_idx];
+
+                        for (int i = 0; i < n_seq_tokens; ++i) {
+                            llama_pos pos_i = ubatch->pos[s * n_seq_tokens + i];
+                            int pos_rel_i = pos_i - p0[seq_idx];
+
+                            int index = pos_rel_j - pos_rel_i;
+                            float s_index = index >= 0 ? -slopes[h] * index : -INFINITY;
+                            data[seq_idx * n_head * n_seq_tokens * n_seq_tokens + h * n_seq_tokens * n_seq_tokens + j * n_seq_tokens + i] = s_index;
+                        }
+                    }
+                }
+            }
+        }
+    }
+
+    bool can_reuse(const llm_graph_params & params) override {
+        bool res = true;
+
+        if (params.ubatch.n_seq_tokens > 1) {
+            res &= (   inp_q_decay &&    inp_q_decay->ne[2] == params.ubatch.n_seq_tokens);
+            res &= (   inp_k_decay &&    inp_k_decay->ne[2] == params.ubatch.n_seq_tokens);
+            res &= (inp_diag_decay && inp_diag_decay->ne[1] == params.ubatch.n_seq_tokens);
+        }
+
+        return res;
+    }
+
+    const llama_hparams & hparams;
+
+    ggml_tensor * inp_slopes     = nullptr; // F32 [n_head]
+    ggml_tensor * inp_q_decay    = nullptr; // F32 [1, n_head, n_batch]
+    ggml_tensor * inp_k_decay    = nullptr; // F32 [1, n_head, n_batch]
+    ggml_tensor * inp_diag_decay = nullptr; // F32 [n_batch, n_batch, n_head]
+};
+
+llama_model_minimax_01::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+    const int64_t n_embd_head = hparams.n_embd_head_v();
+
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
+    // GGML_ASSERT(n_embd_head == n_rot); this is wrong in case of minimax, head_dim = 128, n_rot = 64
+
+    const int64_t n_seqs  = ubatch.n_seqs;
+    const int64_t n_seq_tokens = ubatch.n_seq_tokens;
+
+    GGML_ASSERT(n_seqs != 0);
+    GGML_ASSERT(ubatch.equal_seqs());
+    GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs);
+
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+
+    auto * inp_hybrid = build_inp_mem_hybrid();
+    auto * inp_rs = inp_hybrid->get_recr();
+
+    ggml_tensor * inp_pos = build_inp_pos();
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    llm_graph_input_la * la = nullptr;
+
+    auto inp = std::make_unique<llm_graph_input_la>(hparams);
+
+    inp->inp_slopes = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, n_head);
+    ggml_set_input(inp->inp_slopes);
+    cb(inp->inp_slopes, "slopes", -1);
+
+    if (n_seq_tokens != 1) {
+        inp->inp_q_decay = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, 1, n_head, n_seq_tokens, n_seqs);
+        ggml_set_input(inp->inp_q_decay);
+        cb(inp->inp_q_decay, "q_decay_exp", -1);
+
+        inp->inp_k_decay = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, 1, n_head, n_seq_tokens, n_seqs);
+        ggml_set_input(inp->inp_k_decay);
+        cb(inp->inp_k_decay, "k_decay_exp", -1);
+
+        inp->inp_diag_decay = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_seq_tokens, n_seq_tokens, n_head, n_seqs);
+        ggml_set_input(inp->inp_diag_decay);
+        cb(inp->inp_diag_decay, "diag_decay_exp", -1);
+    }
+
+    la = (llm_graph_input_la *) res->add_input(std::move(inp));
+
+    ggml_tensor * slopes = la->inp_slopes;
+
+    for (int il = 0; il < n_layer; ++il) {
+        res->t_layer_inp[il] = inpL;
+
+        ggml_tensor * inpSA = inpL;
+
+        cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
+        cb(cur, "attn_norm", il);
+
+        ggml_tensor * residual = cur;
+
+        // self_attention
+        if (!hparams.is_recr(il)) {
+            // softmax attention layer
+
+            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,
+                    n_embd_head, n_head, n_head_kv, il);
+
+            Qcur = ggml_rope_ext(
+                ctx0, Qcur, inp_pos, nullptr,
+                n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                ext_factor, attn_factor, beta_fast, beta_slow
+                );
+
+            Kcur = ggml_rope_ext(
+                ctx0, Kcur, inp_pos, nullptr,
+                n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                ext_factor, attn_factor, beta_fast, beta_slow
+                );
+
+            cb(Qcur, "Qcur", il);
+            cb(Kcur, "Kcur", il);
+            cb(Vcur, "Vcur", il);
+
+            cur = build_attn(inp_hybrid->get_attn(),
+                    model.layers[il].wo, NULL, model.layers[il].wo_s,
+                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+        } else {
+            // lightning attention layer
+
+            const auto * mctx_cur = inp_rs->mctx;
+            const auto kv_head = mctx_cur->get_head();
+
+            // TODO unneeded - any way to make conv states optional in recurrent memory?
+            ggml_tensor * conv_states_all = mctx_cur->get_r_l(il);
+            ggml_tensor * conv_state_all  = build_rs(inp_rs, conv_states_all, hparams.n_embd_r(), n_seqs);
+            ggml_build_forward_expand(gf, conv_state_all);
+
+            float slope_scale = 1.0 - 1.0 * il / (n_layer - 1) + 1e-5;
+            ggml_tensor * slope_rate = ggml_scale(ctx0, slopes, slope_scale);
+            cb(slope_rate, "slope_rate", il);
+
+            cur = ggml_reshape_4d(ctx0, cur, cur->ne[0], n_seq_tokens, 1, n_seqs);
+
+            ggml_tensor * QKVcur = build_lora_mm(model.layers[il].wqkv, cur);
+            cb(QKVcur, "QKVcur", il);
+
+            QKVcur = ggml_silu(ctx0, QKVcur);
+            cb(QKVcur, "QKVcur_silu", il);
+
+            QKVcur = ggml_reshape_4d(ctx0, QKVcur, n_embd_head * 3, n_head, n_seq_tokens, n_seqs);
+
+            ggml_tensor * Qcur = ggml_view_4d(ctx0, QKVcur, n_embd_head, n_head, n_seq_tokens, n_seqs, QKVcur->nb[1], QKVcur->nb[2], QKVcur->nb[3], 0*ggml_element_size(QKVcur)*n_embd_head);
+            ggml_tensor * Kcur = ggml_view_4d(ctx0, QKVcur, n_embd_head, n_head, n_seq_tokens, n_seqs, QKVcur->nb[1], QKVcur->nb[2], QKVcur->nb[3], 1*ggml_element_size(QKVcur)*n_embd_head);
+            ggml_tensor * Vcur = ggml_view_4d(ctx0, QKVcur, n_embd_head, n_head, n_seq_tokens, n_seqs, QKVcur->nb[1], QKVcur->nb[2], QKVcur->nb[3], 2*ggml_element_size(QKVcur)*n_embd_head);
+
+            cb(Qcur, "Qcur", il);
+            cb(Kcur, "Kcur", il);
+            cb(Vcur, "Vcur", il);
+
+            // get previous KV
+            ggml_tensor * la_states_all = mctx_cur->get_s_l(il);
+            ggml_tensor * state = build_rs(inp_rs, la_states_all, hparams.n_embd_s(), n_seqs);
+
+            ggml_tensor * kv_old = ggml_reshape_4d(ctx0, state, n_embd_head, n_embd_head, n_head, n_seqs);
+            cb(kv_old, "kv_old", il);
+
+            ggml_tensor * qkv = nullptr;
+            ggml_tensor * kv_new = nullptr;
+
+            if (n_seq_tokens == 1) {
+                // lightning attention - optimized single token case for TG
+
+                ggml_tensor * slopes_neg = ggml_scale(ctx0, slope_rate, -1.0);
+                cb(slopes_neg, "slopes_neg", il);
+
+                ggml_tensor * ratio = ggml_exp(ctx0, slopes_neg);
+                cb(ratio, "ratio", il);
+
+                ggml_tensor * ratio_3d = ggml_reshape_3d(ctx0, ratio, 1, 1, n_head);
+                cb(ratio_3d, "ratio3d", il);
+
+                ggml_tensor * v_trans = ggml_cont(ctx0, ggml_permute(ctx0, Vcur, 1, 2, 0, 3));
+                cb(v_trans, "v_trans", il);
+
+                ggml_tensor * k_trans = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 1, 2, 0, 3));
+                cb(k_trans, "k_trans", il);
+
+                ggml_tensor * kv_cur = ggml_mul_mat(ctx0, k_trans, v_trans);
+                cb(kv_cur, "kv_cur", il);
+
+                ggml_tensor * kv_old_s = ggml_mul(ctx0, kv_old, ratio_3d);
+                cb(kv_old_s, "kv_old_s", il);
+
+                kv_new = ggml_add(ctx0, kv_old_s, kv_cur);
+                cb(kv_new, "kv_new", il);
+
+                ggml_tensor * q_trans = ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
+                cb(q_trans, "q_trans", il);
+
+                qkv = ggml_mul_mat(ctx0, kv_new, q_trans);
+                cb(qkv, "qkv", il);
+            } else if(n_seq_tokens > 1) {
+                // lightning attention - general multi token case for PP
+
+                ggml_tensor *    q_decay_exp = la->inp_q_decay;
+                ggml_tensor *    k_decay_exp = la->inp_k_decay;
+                ggml_tensor * diag_decay_exp = la->inp_diag_decay;
+
+                ggml_tensor *    q_decay = ggml_exp(ctx0, ggml_scale(ctx0, q_decay_exp, slope_scale));
+                cb(q_decay, "q_decay", il);
+                ggml_tensor *    k_decay = ggml_exp(ctx0, ggml_scale(ctx0, k_decay_exp, slope_scale));
+                cb(k_decay, "k_decay", il);
+                ggml_tensor * diag_decay = ggml_exp(ctx0, ggml_scale(ctx0, diag_decay_exp, slope_scale));
+                cb(diag_decay, "diag_decay", il);
+
+                ggml_tensor * q_s = ggml_mul(ctx0, Qcur, q_decay);
+                cb(q_s, "q_s", il);
+
+                ggml_tensor * q_s_trans = ggml_permute(ctx0, q_s, 0, 2, 1, 3);
+                cb(q_s_trans, "q_s_trans", il);
+
+                ggml_tensor * qkv_none_diag = ggml_mul_mat(ctx0, kv_old, q_s_trans);
+                cb(qkv_none_diag, "qkv_none_diag", il);
+
+                ggml_tensor * q_trans = ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
+                cb(q_trans, "q_trans", il);
+
+                ggml_tensor * k_trans = ggml_permute(ctx0, Kcur, 0, 2, 1, 3);
+                cb(k_trans, "k_trans", il);
+
+                ggml_tensor * qk = ggml_mul_mat(ctx0, k_trans, q_trans);
+                cb(qk, "qk", il);
+
+                qk = ggml_mul(ctx0, qk, diag_decay);
+                cb(qk, "qk_s", il);
+
+                ggml_tensor * v_trans = ggml_cont(ctx0, ggml_permute(ctx0, Vcur, 1, 2, 0, 3));
+                cb(v_trans, "v_trans", il);
+
+                ggml_tensor * qkv_diag = ggml_mul_mat(ctx0, v_trans, qk);
+                cb(qkv_diag, "qkv_diag", il);
+
+                qkv = ggml_add(ctx0, qkv_none_diag, qkv_diag);
+                cb(qkv, "qkv", il);
+
+                ggml_build_forward_expand(gf, qkv);
+
+                ggml_tensor * slopes_neg = ggml_scale(ctx0, slope_rate, -1.0*n_seq_tokens);
+                cb(slopes_neg, "slopes_neg", il);
+
+                ggml_tensor * block_decay = ggml_exp(ctx0, slopes_neg);
+                cb(block_decay, "block_decay", il);
+
+                ggml_tensor * block_decay_3d = ggml_reshape_3d(ctx0, block_decay, 1, 1, n_head);
+                cb(block_decay_3d, "block_decay_3d", il);
+
+                ggml_tensor * kv_old_s = ggml_mul(ctx0, kv_old, block_decay_3d);
+                cb(kv_old_s, "kv_old_s", il);
+
+                ggml_tensor * k_after_decay = ggml_mul(ctx0, Kcur, k_decay);
+                cb(k_after_decay, "k_after_decay", il);
+
+                ggml_tensor * k_after_decay_trans = ggml_cont(ctx0, ggml_permute(ctx0, k_after_decay, 1, 2, 0, 3));
+                cb(k_after_decay_trans, "k_after_decay_trans", il);
+
+                ggml_tensor * kv_cur = ggml_mul_mat(ctx0, k_after_decay_trans, v_trans);
+                cb(kv_cur, "kv_cur", il);
+
+                kv_new = ggml_add(ctx0, kv_old_s, kv_cur);
+                cb(kv_new, "kv_new", il);
+            }
+
+            // store new KV
+            ggml_build_forward_expand(gf,
+                                     ggml_cpy(ctx0, kv_new,
+                                              ggml_view_1d(ctx0, la_states_all, hparams.n_embd_s() * n_seqs,
+                                                           kv_head * hparams.n_embd_s() * ggml_element_size(la_states_all))));
+
+            qkv = ggml_cont(ctx0, ggml_permute(ctx0, qkv, 0, 2, 1, 3));
+            cb(qkv, "qkv_permuted", il);
+
+            qkv = ggml_reshape_4d(ctx0, qkv, qkv->ne[0]*qkv->ne[1], qkv->ne[2], 1, qkv->ne[3]);
+
+            // norm
+            ggml_tensor * qkv_norm = build_norm(qkv,
+                    model.layers[il].attn_norm_2, NULL,
+                    LLM_NORM_RMS, il);
+            cb(qkv_norm, "qkv_norm", il);
+
+            ggml_tensor * g = build_lora_mm(model.layers[il].wg, cur);
+            cb(g, "g", il);
+
+            g = ggml_sigmoid(ctx0, g);
+            cb(g, "g_sigm", il);
+
+            cur = ggml_mul(ctx0, g, qkv_norm);
+
+            cur = build_lora_mm(model.layers[il].wo, cur);
+            cb(cur, "attn_out", il);
+
+            cur = ggml_reshape_2d(ctx0, cur, cur->ne[0], n_seq_tokens*n_seqs);
+            cb(cur, "attn_out", il);
+        }
+
+        if (il == n_layer - 1 && inp_out_ids) {
+            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            residual = ggml_get_rows(ctx0, residual, inp_out_ids);
+        }
+
+        residual = ggml_scale(ctx0, residual, hparams.f_residual_scale);
+        cb(residual, "residual_scaled_attn", il);
+
+        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, residual);
+        cb(ffn_inp, "ffn_inp", il);
+
+        // MoE branch
+        cur = build_norm(ffn_inp,
+                model.layers[il].ffn_norm, NULL,
+                LLM_NORM_RMS, il);
+        cb(cur, "ffn_norm", il);
+
+        residual = cur;
+
+        cur = build_moe_ffn(cur,
+                model.layers[il].ffn_gate_inp,
+                model.layers[il].ffn_up_exps,
+                model.layers[il].ffn_gate_exps,
+                model.layers[il].ffn_down_exps,
+                model.layers[il].ffn_exp_probs_b,
+                n_expert, n_expert_used,
+                LLM_FFN_SILU, true,
+                hparams.expert_weights_scale,
+                LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
+                il);
+        cb(cur, "ffn_moe_out", il);
+
+        residual = ggml_scale(ctx0, residual, hparams.f_residual_scale);
+        cb(residual, "residual_scaled_ffn", il);
+
+        cur = ggml_add(ctx0, cur, residual);
+        cb(cur, "ffn_out", il);
+
+        cur = build_cvec(cur, il);
+        cb(cur, "l_out", il);
+
+        // input for next layer
+        inpL = cur;
+    }
+
+    cur = inpL;
+
+    cur = build_norm(cur,
+            model.output_norm, NULL,
+            LLM_NORM_RMS, -1);
+
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    // lm_head
+    cur = build_lora_mm(model.output, cur, model.output_s);
+
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}

src/models/minimax-m3.cpp

@@ -25,6 +25,8 @@ void llama_model_minimax_m3::load_arch_hparams(llama_model_loader & ml) {
     ml.get_key(LLM_KV_ATTENTION_INDEXER_LOCAL_BLOCKS,  hparams.indexer_local_blocks);
     msa_p = { (int) hparams.indexer_block_size, (int) hparams.indexer_top_k, (int) hparams.indexer_local_blocks };
 
+    GGML_ASSERT(hparams.indexer_block_size > 0); // avoid div by zero
+
     switch (hparams.n_layer()) {
         case 60: type = LLM_TYPE_428B_A23B; break;
         default: type = LLM_TYPE_UNKNOWN;

src/models/models.h

@@ -2043,6 +2043,19 @@ struct llama_model_apertus : public llama_model_base {
 };
 
 
+struct llama_model_minimax_01 : public llama_model_base {
+    llama_model_minimax_01(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
+};
+
+
 struct llama_model_minimax_m2 : public llama_model_base {
     llama_model_minimax_m2(const struct llama_model_params & params) : llama_model_base(params) {}
     void load_arch_hparams(llama_model_loader & ml) override;
@@ -2272,6 +2285,42 @@ struct llama_model_mimo2 : public llama_model_base {
 };
 
 
+struct llama_model_kimi_k3 : public llama_model_base {
+    llama_model_kimi_k3(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_build_delta_net_base {
+        graph(const llama_model & model, const llm_graph_params & params);
+
+        const llama_model & model;
+
+        // Cross-layer residual attention (K3's `_apply_attn_res`).
+        ggml_tensor * resi_stack = nullptr;
+
+        void          res_push(ggml_tensor * cur, int64_t n_embd, int64_t n_tokens);
+        ggml_tensor * res_mix(ggml_tensor * cur, ggml_tensor * score_w,
+                              int64_t n_tokens, int il);
+
+        ggml_tensor * build_kda_layer(ggml_tensor * cur, const llama_layer & layer,
+                                      llm_graph_input_rs * inp_rs,
+                                      int64_t d_conv, int64_t head_dim, int64_t n_head_kda,
+                                      int64_t d_inner, int64_t n_seq_tokens, int64_t n_seqs, int il);
+
+        ggml_tensor * build_mla_layer(ggml_tensor * cur, const llama_layer & layer,
+                                      llm_graph_input_attn_k  * inp_attn_k,
+                                      llm_graph_input_attn_kv * inp_attn_kv,
+                                      int64_t n_embd_head_k_mla, int64_t n_embd_head_v_mla,
+                                      int64_t kv_lora_rank, int64_t n_embd_head_qk_rope,
+                                      int64_t n_embd_head_qk_nope, float kq_scale, int il);
+
+        ggml_tensor * build_latent_moe(ggml_tensor * cur, const llama_layer & layer,
+                                       int64_t n_embd_latent, int il);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
+};
+
 struct llama_model_kimi_linear : public llama_model_base {
     llama_model_kimi_linear(const struct llama_model_params & params) : llama_model_base(params) {}
     void load_arch_hparams(llama_model_loader & ml) override;

tests/test-chat.cpp

@@ -4462,6 +4462,109 @@ static void test_template_output_peg_parsers(bool detailed_debug) {
         }
     }
 
+    // Kimi-K3 tests - custom parser
+    // Unique feature: XTML tags built from <|open|>/<|close|>/<|sep|>, and a
+    // generation prompt that leaves the think section already open.
+    {
+        auto tst = peg_tester("models/templates/Kimi-K3.jinja", detailed_debug);
+
+        // Content only. The response section is explicit even with no reasoning.
+        tst.test("<|open|>response<|sep|>Hello, world!\nWhat's up?<|close|>response<|sep|>"
+                 "<|close|>message<|sep|>")
+            .expect(message_assist)
+            .run();
+
+        // Reasoning with no opening tag - the generation prompt already opened it
+        tst.test("I'm thinking about this<|close|>think<|sep|>"
+                 "<|open|>response<|sep|>Hello, world!\nWhat's up?<|close|>response<|sep|>"
+                 "<|close|>message<|sep|>")
+            .reasoning_format(COMMON_REASONING_FORMAT_AUTO)
+            .expect(simple_assist_msg("Hello, world!\nWhat's up?", "I'm thinking about this"))
+            .run();
+
+        // Prose that mentions the tag names must survive intact.
+        tst.test("<|open|>response<|sep|>Use the response tag, then message the handler."
+                 "<|close|>response<|sep|><|close|>message<|sep|>")
+            .expect(simple_assist_msg("Use the response tag, then message the handler."))
+            .run();
+
+        // Truncated mid-reasoning (hit the token budget): keep the reasoning.
+        tst.test("I was still thinking when the budget ran out")
+            .reasoning_format(COMMON_REASONING_FORMAT_AUTO)
+            .expect_reasoning("I was still thinking when the budget ran out")
+            .run();
+
+        // Single tool call, one argument.
+        tst.test("<|open|>response<|sep|><|close|>response<|sep|>"
+                 "<|open|>tools<|sep|>"
+                 "<|open|>call tool=\"special_function\" index=\"1\"<|sep|>"
+                 "<|open|>argument key=\"arg1\" type=\"number\"<|sep|>1<|close|>argument<|sep|>"
+                 "<|close|>call<|sep|><|close|>tools<|sep|><|close|>message<|sep|>")
+            .tools({ special_function_tool })
+            .expect_tool_calls({
+                { "special_function", R"({"arg1":1})", "" },
+            })
+            .run();
+
+        // Tool call preceded by reasoning (no opening think tag) and content.
+        tst.test("I should call it<|close|>think<|sep|>"
+                 "<|open|>response<|sep|>On it.<|close|>response<|sep|>"
+                 "<|open|>tools<|sep|>"
+                 "<|open|>call tool=\"special_function\" index=\"1\"<|sep|>"
+                 "<|open|>argument key=\"arg1\" type=\"number\"<|sep|>1<|close|>argument<|sep|>"
+                 "<|close|>call<|sep|><|close|>tools<|sep|><|close|>message<|sep|>")
+            .reasoning_format(COMMON_REASONING_FORMAT_AUTO)
+            .tools({ special_function_tool })
+            .expect(simple_assist_msg("On it.", "I should call it", "special_function",
+                                      R"({"arg1":1})", ""))
+            .run();
+
+        // Multiple typed arguments: values must come back as JSON numbers, not strings
+        tst.test("<|open|>response<|sep|><|close|>response<|sep|>"
+                 "<|open|>tools<|sep|>"
+                 "<|open|>call tool=\"special_function_with_opt\" index=\"1\"<|sep|>"
+                 "<|open|>argument key=\"arg1\" type=\"number\"<|sep|>1<|close|>argument<|sep|>"
+                 "<|open|>argument key=\"arg2\" type=\"number\"<|sep|>2<|close|>argument<|sep|>"
+                 "<|close|>call<|sep|><|close|>tools<|sep|><|close|>message<|sep|>")
+            .tools({ special_function_tool_with_optional_param })
+            .expect_tool_calls({
+                { "special_function_with_opt", R"({"arg1":1,"arg2":2})", "" },
+            })
+            .run();
+
+        // Parallel tool calls in one <|open|>tools<|sep|> section.
+        tst.test("<|open|>response<|sep|><|close|>response<|sep|>"
+                 "<|open|>tools<|sep|>"
+                 "<|open|>call tool=\"special_function\" index=\"1\"<|sep|>"
+                 "<|open|>argument key=\"arg1\" type=\"number\"<|sep|>1<|close|>argument<|sep|>"
+                 "<|close|>call<|sep|>"
+                 "<|open|>call tool=\"special_function_with_opt\" index=\"2\"<|sep|>"
+                 "<|open|>argument key=\"arg1\" type=\"number\"<|sep|>1<|close|>argument<|sep|>"
+                 "<|open|>argument key=\"arg2\" type=\"number\"<|sep|>2<|close|>argument<|sep|>"
+                 "<|close|>call<|sep|><|close|>tools<|sep|><|close|>message<|sep|>")
+            .parallel_tool_calls(true)
+            .tools({ special_function_tool, special_function_tool_with_optional_param })
+            .expect_tool_calls({
+                { "special_function", R"({"arg1":1})", "" },
+                { "special_function_with_opt", R"({"arg1":1,"arg2":2})", "" },
+            })
+            .run();
+
+        // String-typed argument keeps its literal text (no JSON coercion).
+        tst.test("<|open|>response<|sep|><|close|>response<|sep|>"
+                 "<|open|>tools<|sep|>"
+                 "<|open|>call tool=\"python\" index=\"1\"<|sep|>"
+                 "<|open|>argument key=\"code\" type=\"string\"<|sep|>print('hey')"
+                 "<|close|>argument<|sep|>"
+                 "<|close|>call<|sep|><|close|>tools<|sep|><|close|>message<|sep|>")
+            .tools({ python_tool })
+            .expect_tool_calls({
+                // custom delimiter: the payload itself contains )"
+                { "python", R"JSON({"code":"print('hey')"})JSON", "" },
+            })
+            .run();
+    }
+
     // Kimi-K2-Thinking tests - custom parser
     // Unique feature: tool call ID embeds function name as functions.<name>:<counter>
     {
@@ -6955,6 +7058,24 @@ static void test_reasoning_budget_message_per_request() {
     }
 }
 
+static void test_reasoning_effort_caps() {
+    LOG_DBG("%s\n", __func__);
+
+    auto assert_supports_effort = [](const std::string & path, bool expected) {
+        auto tmpls = read_templates(path);
+        assert_equals(expected, common_chat_templates_get_caps(tmpls.get()).at("supports_reasoning_effort"));
+    };
+
+    assert_supports_effort("models/templates/deepseek-ai-DeepSeek-V4.jinja", true);
+    assert_supports_effort("models/templates/muse-glimmer.jinja", true);
+    assert_supports_effort("models/templates/tencent-Hy3.jinja", true);
+    assert_supports_effort("models/templates/openai-gpt-oss-120b.jinja", true);
+    assert_supports_effort("models/templates/upstage-Solar-Open-100B.jinja", true);
+    assert_supports_effort("models/templates/Cohere2MoE.jinja", true);
+    assert_supports_effort("models/templates/meta-llama-Llama-3.1-8B-Instruct.jinja", false);
+    assert_supports_effort("models/templates/Qwen-Qwen3-0.6B.jinja", false);
+}
+
 static void test_msg_diffs_compute() {
     LOG_DBG("%s\n", __func__);
     {
@@ -7114,6 +7235,7 @@ int main(int argc, char ** argv) {
         test_deepseek_v4_thinking_retention();
         test_deepseek_v4_tool_result_ordering();
         test_template_generation_prompt();
+        test_reasoning_effort_caps();
         test_reasoning_budget_tokens_per_request();
         test_reasoning_budget_message_per_request();
         test_template_output_peg_parsers(detailed_debug);

tests/test-jinja.cpp

@@ -10,6 +10,7 @@
 #include "jinja/parser.h"
 #include "jinja/lexer.h"
 #include "jinja/utils.h"
+#include "jinja/caps.h"
 
 #include "testing.h"
 
@@ -33,6 +34,8 @@ static void test_array_methods(testing & t);
 static void test_object_methods(testing & t);
 static void test_hasher(testing & t);
 static void test_stats(testing & t);
+static void test_caps(testing & t);
+static void test_string_parts(testing & t);
 static void test_fuzzing(testing & t);
 
 static bool g_python_mode = false;
@@ -72,6 +75,8 @@ int main(int argc, char *argv[]) {
     if (!g_python_mode) {
         t.test("hasher", test_hasher);
         t.test("stats", test_stats);
+        t.test("caps", test_caps);
+        t.test("string parts", test_string_parts);
         t.test("fuzzing", test_fuzzing);
     }
 
@@ -2057,6 +2062,81 @@ static void test_stats(testing & t) {
     });
 }
 
+static void test_caps(testing & t) {
+    static auto get_caps = [](const std::string & tmpl) -> jinja::caps {
+        jinja::lexer lexer;
+        auto lexer_res = lexer.tokenize(tmpl);
+
+        jinja::program prog = jinja::parse_from_tokens(lexer_res);
+
+        return jinja::caps_get(prog);
+    };
+
+    t.test("string content", [](testing & t) {
+        auto caps = get_caps(
+            "{% for message in messages %}"
+            "{{ message['role'] + ': ' + message['content'] }}"
+            "{% endfor %}"
+        );
+        t.assert_true("supports string content", caps.supports_string_content);
+        t.assert_true("does not support typed content", !caps.supports_typed_content);
+    });
+
+    t.test("typed content, raises on string", [](testing & t) {
+        // 'selectattr' is not a String filter, so it throws
+        auto caps = get_caps(
+            "{% for message in messages %}"
+            "{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}"
+            "{{ content['text'] }}"
+            "{% endfor %}"
+            "{% endfor %}"
+        );
+        t.assert_true("does not support string content", !caps.supports_string_content);
+        t.assert_true("supports typed content", caps.supports_typed_content);
+    });
+
+    t.test("typed content, silently drops string", [](testing & t) {
+        // no throw here, but content[0]['text'] is undefined for a string (MiniMax-M1 case)
+        auto caps = get_caps(
+            "{% for message in messages %}"
+            "{{ message['content'][0]['text'] }}"
+            "{% endfor %}"
+        );
+        t.assert_true("does not support string content", !caps.supports_string_content);
+        t.assert_true("supports typed content", caps.supports_typed_content);
+    });
+}
+
+static void test_string_parts(testing & t) {
+    static auto render = [](const std::string & tmpl, const json & vars) -> jinja::string {
+        jinja::lexer lexer;
+        auto lexer_res = lexer.tokenize(tmpl);
+
+        jinja::program ast = jinja::parse_from_tokens(lexer_res);
+
+        jinja::context ctx(tmpl);
+        jinja::global_from_json(ctx, vars, true);
+
+        jinja::runtime runtime(ctx);
+        return runtime.gather_string_parts(runtime.execute(ast))->as_string();
+    };
+
+    t.test("merge joins only the neighbours with the same type", [](testing & t) {
+        // "AB" comes from the input and merges, "-" comes from the template and must not
+        jinja::string res = render("{{ val.a }}{{ val.b }}-{{ val.c }}",
+                                   json{{"val", json{{"a", "A"}, {"b", "B"}, {"c", "C"}}}});
+
+        if (t.assert_true("3 parts after the merge", res.parts.size() == 3)) {
+            t.assert_true("part 0 is the merged input", res.parts[0].val == "AB" && res.parts[0].is_input);
+            t.assert_true("part 1 is from the template", res.parts[1].val == "-" && !res.parts[1].is_input);
+            t.assert_true("part 2 is input",             res.parts[2].val == "C" && res.parts[2].is_input);
+        } else {
+            t.log("parts: " + std::to_string(res.parts.size()) + ", rendered: " + json(res.str()).dump());
+        }
+    });
+
+}
+
 static void test_template_cpp(testing & t, const std::string & name, const std::string & tmpl, const json & vars, const std::string & expect) {
     t.test(name, [&tmpl, &vars, &expect](testing & t) {
         jinja::lexer lexer;
@@ -2084,8 +2164,7 @@ static void test_template_cpp(testing & t, const std::string & name, const std::
                 t.log("Actual  : " + json(rendered).dump());
             }
         } catch (const jinja::not_implemented_exception & e) {
-            // TODO @ngxson : remove this when the test framework supports skipping tests
-            t.log("Skipped: " + std::string(e.what()));
+            t.skip(e.what());
         }
     });
 }

tests/test-llama-archs.cpp

@@ -105,6 +105,7 @@ static gguf_context_ptr get_gguf_ctx(const llm_arch arch, const bool moe) {
             || arch == LLM_ARCH_DEEPSEEK32
             || arch == LLM_ARCH_GLM_DSA
             || arch == LLM_ARCH_KIMI_LINEAR
+            || arch == LLM_ARCH_KIMI_K3
             || arch == LLM_ARCH_MISTRAL4) {
         n_embd = 128;
         n_head = 1;
@@ -145,7 +146,7 @@ static gguf_context_ptr get_gguf_ctx(const llm_arch arch, const bool moe) {
     ms.add_kv(LLM_KV_FULL_ATTENTION_INTERVAL, uint32_t(2));
 
     if (arch == LLM_ARCH_PLAMO2 || arch == LLM_ARCH_JAMBA || arch == LLM_ARCH_NEMOTRON_H || arch == LLM_ARCH_NEMOTRON_H_MOE ||
-            arch == LLM_ARCH_GRANITE_HYBRID || arch == LLM_ARCH_LFM2 || arch == LLM_ARCH_LFM2MOE || arch == LLM_ARCH_KIMI_LINEAR) {
+            arch == LLM_ARCH_GRANITE_HYBRID || arch == LLM_ARCH_LFM2 || arch == LLM_ARCH_LFM2MOE || arch == LLM_ARCH_KIMI_LINEAR || arch == LLM_ARCH_KIMI_K3) {
         GGML_ASSERT(n_layer >= 2);
         std::vector<uint32_t> n_head_per_layer;
         n_head_per_layer.reserve(n_layer);
@@ -164,6 +165,7 @@ static gguf_context_ptr get_gguf_ctx(const llm_arch arch, const bool moe) {
             || arch == LLM_ARCH_DEEPSEEK32
             || arch == LLM_ARCH_GLM_DSA
             || arch == LLM_ARCH_KIMI_LINEAR
+            || arch == LLM_ARCH_KIMI_K3
             || arch == LLM_ARCH_MISTRAL4) {
         ms.add_kv(LLM_KV_ATTENTION_KEY_LENGTH,       uint32_t(576));
         ms.add_kv(LLM_KV_ATTENTION_VALUE_LENGTH,     uint32_t(512));
@@ -218,6 +220,7 @@ static gguf_context_ptr get_gguf_ctx(const llm_arch arch, const bool moe) {
     if (moe) {
         ms.add_kv(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, n_ff);
         ms.add_kv(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, n_ff / 2);  // distinct from n_ff so a saver key-clobber surfaces on reload
+        ms.add_kv(LLM_KV_EXPERT_LATENT_LENGTH,       n_ff);
         ms.add_kv(LLM_KV_INTERLEAVE_MOE_LAYER_STEP,  uint32_t(2));
         ms.add_kv(LLM_KV_EXPERT_COUNT,               uint32_t(2));
         ms.add_kv(LLM_KV_EXPERT_USED_COUNT,          uint32_t(1));
@@ -243,6 +246,11 @@ static gguf_context_ptr get_gguf_ctx(const llm_arch arch, const bool moe) {
     ms.add_kv(LLM_KV_KDA_HEAD_DIM,              uint32_t(128));
     ms.add_kv(LLM_KV_WKV_HEAD_SIZE,             n_embd/n_head);
     ms.add_kv(LLM_KV_SHORTCONV_L_CACHE,         uint32_t(3));
+    ms.add_kv(LLM_KV_RESIDUAL_SCALE,            3.5565588200778455f);
+    ms.add_kv(LLM_KV_ATTN_RES_BLOCK_SIZE,       uint32_t(12));
+    ms.add_kv(LLM_KV_ACTIVATION_SITU_BETA,      4.0f);
+    ms.add_kv(LLM_KV_ACTIVATION_SITU_LINEAR_BETA, 25.0f);
+    ms.add_kv(LLM_KV_KDA_GATE_LOWER_BOUND,      -5.0f);
 
     for (uint32_t il = 0; il < n_layer; il++) {
         ggml_tensor t;
@@ -364,12 +372,14 @@ static bool moe_mandatory(const llm_arch arch) {
         case LLM_ARCH_SMALLTHINKER:
         case LLM_ARCH_LLADA_MOE:
         case LLM_ARCH_GROVEMOE:
+        case LLM_ARCH_MINIMAX_01:
         case LLM_ARCH_MINIMAX_M2:
         case LLM_ARCH_MINIMAX_M3:
         case LLM_ARCH_RND1:
         case LLM_ARCH_PADDLEOCR:
         case LLM_ARCH_MIMO2:
         case LLM_ARCH_KIMI_LINEAR:
+        case LLM_ARCH_KIMI_K3:
         case LLM_ARCH_STEP35:
         case LLM_ARCH_MISTRAL4:
         case LLM_ARCH_MELLUM:
@@ -436,7 +446,7 @@ static bool arch_supported(const llm_arch arch) {
 
     // FIXME: these hit scheduler/view-backed-output issues with WebGPU on CI.
 #ifdef GGML_USE_WEBGPU
-    if (arch == LLM_ARCH_DEEPSEEK32 || arch == LLM_ARCH_GLM_DSA) {
+    if (arch == LLM_ARCH_DEEPSEEK32 || arch == LLM_ARCH_GLM_DSA || arch == LLM_ARCH_MINIMAX_01) {
         return false;
     }
 #endif // GGML_USE_WEBGPU

tests/testing.h

@@ -21,6 +21,11 @@ struct testing {
     int failures = 0;
     int unnamed = 0;
     int exceptions = 0;
+    int skipped = 0;
+
+    // set by skip(), read by the innermost test()
+    bool skip_current = false;
+    std::string skip_reason;
 
     static constexpr std::size_t status_column = 80;
 
@@ -78,7 +83,12 @@ struct testing {
         }
     }
 
-    void print_result(const std::string &label, int new_failures, int new_assertions, const std::string &extra = "") const {
+    void skip(const std::string &reason = "") {
+        skip_current = true;
+        skip_reason  = reason;
+    }
+
+    void print_result(const std::string &label, int new_failures, int new_assertions, const std::string &extra = "", bool was_skipped = false) const {
         std::string line = indent() + label;
 
         std::string details;
@@ -101,7 +111,7 @@ struct testing {
             line += " (" + details + ")";
         }
 
-        std::string status = (new_failures == 0) ? "[PASS]" : "[FAIL]";
+        std::string status = new_failures != 0 ? "[FAIL]" : (was_skipped ? "[SKIP]" : "[PASS]");
 
         if (line.size() + 1 < status_column) {
             line.append(status_column - line.size(), ' ');
@@ -126,12 +136,26 @@ struct testing {
         int before_failures   = failures;
         int before_assertions = assertions;
 
+        // do not let a skipped subtest also mark its parent as skipped
+        bool        outer_skip        = skip_current;
+        std::string outer_skip_reason = skip_reason;
+        skip_current = false;
+        skip_reason.clear();
+
         run_with_exceptions([&] { f(*this); }, "test");
 
         int new_failures   = failures   - before_failures;
         int new_assertions = assertions - before_assertions;
 
-        print_result(name, new_failures, new_assertions);
+        bool was_skipped = skip_current && new_failures == 0;
+        if (was_skipped) {
+            ++skipped;
+        }
+
+        print_result(name, new_failures, new_assertions, was_skipped ? skip_reason : "", was_skipped);
+
+        skip_current = outer_skip;
+        skip_reason  = outer_skip_reason;
 
         stack.pop_back();
     }
@@ -238,6 +262,7 @@ struct testing {
         out << "assertions : " << assertions << "\n";
         out << "failures   : " << failures << "\n";
         out << "exceptions : " << exceptions << "\n";
+        out << "skipped    : " << skipped << "\n";
         return failures == 0 ? 0 : 1;
     }
 };

tools/cli/README.md

@@ -170,6 +170,7 @@
 | `--jinja, --no-jinja` | whether to use jinja template engine for chat (default: enabled)<br/>(env: LLAMA_ARG_JINJA) |
 | `--reasoning-format FORMAT` | controls whether thought tags are allowed and/or extracted from the response, and in which format they're returned; one of:<br/>- none: leaves thoughts unparsed in `message.content`<br/>- deepseek: puts thoughts in `message.reasoning_content`<br/>- deepseek-legacy: keeps `<think>` tags in `message.content` while also populating `message.reasoning_content`<br/>(default: auto)<br/>(env: LLAMA_ARG_THINK) |
 | `-rea, --reasoning [on\|off\|auto]` | Use reasoning/thinking in the chat ('on', 'off', or 'auto', default: 'auto' (detect from template))<br/>(env: LLAMA_ARG_REASONING) |
+| `--reasoning-effort LEVEL` | reasoning effort level given to the chat template: 'default' to keep the template default,<br/>or a level such as 'minimal', 'low', 'medium', 'high', 'xhigh' or 'max' (default: default)<br/>(env: LLAMA_ARG_REASONING_EFFORT) |
 | `--reasoning-budget N` | token budget for thinking: -1 for unrestricted, 0 for immediate end, N>0 for token budget (default: -1)<br/>(env: LLAMA_ARG_THINK_BUDGET) |
 | `--reasoning-budget-message MESSAGE` | message injected before the end-of-thinking tag when reasoning budget is exhausted (default: none)<br/>(env: LLAMA_ARG_THINK_BUDGET_MESSAGE) |
 | `--reasoning-preserve, --no-reasoning-preserve` | preserve reasoning trace in the full history, not just the last assistant message (default: template default)<br/>compatible with certain templates having 'supports_preserve_reasoning' capability<br/>example: https://docs.z.ai/guides/capabilities/thinking-mode#preserved-thinking<br/>(env: LLAMA_ARG_REASONING_PRESERVE) |

tools/completion/README.md

@@ -251,6 +251,7 @@ llama-completion.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --ignore-eos -n -1
 | `--jinja, --no-jinja` | whether to use jinja template engine for chat (default: disabled)<br/>(env: LLAMA_ARG_JINJA) |
 | `--reasoning-format FORMAT` | controls whether thought tags are allowed and/or extracted from the response, and in which format they're returned; one of:<br/>- none: leaves thoughts unparsed in `message.content`<br/>- deepseek: puts thoughts in `message.reasoning_content`<br/>- deepseek-legacy: keeps `<think>` tags in `message.content` while also populating `message.reasoning_content`<br/>(default: auto)<br/>(env: LLAMA_ARG_THINK) |
 | `-rea, --reasoning [on\|off\|auto]` | Use reasoning/thinking in the chat ('on', 'off', or 'auto', default: 'auto' (detect from template))<br/>(env: LLAMA_ARG_REASONING) |
+| `--reasoning-effort LEVEL` | reasoning effort level given to the chat template: 'default' to keep the template default,<br/>or a level such as 'minimal', 'low', 'medium', 'high', 'xhigh' or 'max' (default: default)<br/>(env: LLAMA_ARG_REASONING_EFFORT) |
 | `--reasoning-budget N` | token budget for thinking: -1 for unrestricted, 0 for immediate end, N>0 for token budget (default: -1)<br/>(env: LLAMA_ARG_THINK_BUDGET) |
 | `--reasoning-budget-message MESSAGE` | message injected before the end-of-thinking tag when reasoning budget is exhausted (default: none)<br/>(env: LLAMA_ARG_THINK_BUDGET_MESSAGE) |
 | `--reasoning-preserve, --no-reasoning-preserve` | preserve reasoning trace in the full history, not just the last assistant message (default: template default)<br/>compatible with certain templates having 'supports_preserve_reasoning' capability<br/>example: https://docs.z.ai/guides/capabilities/thinking-mode#preserved-thinking<br/>(env: LLAMA_ARG_REASONING_PRESERVE) |
@@ -523,13 +524,15 @@ These options help improve the performance and memory usage of the LLaMA models.
 -   `-t N, --threads N`: Set the number of threads to use during generation. For optimal performance, it is recommended to set this value to the number of physical CPU cores your system has (as opposed to the logical number of cores). Using the correct number of threads can greatly improve performance.
 -   `-tb N, --threads-batch N`: Set the number of threads to use during batch and prompt processing. In some systems, it is beneficial to use a higher number of threads during batch processing than during generation. If not specified, the number of threads used for batch processing will be the same as the number of threads used for generation.
 
-### Mlock
+### Model Loading Mode
 
--   `--mlock`: Lock the model in memory, preventing it from being swapped out when memory-mapped. This can improve performance but trades away some of the advantages of memory-mapping by requiring more RAM to run and potentially slowing down load times as the model loads into RAM.
-
-### No Memory Mapping
-
--   `--no-mmap`: Do not memory-map the model. By default, models are mapped into memory, which allows the system to load only the necessary parts of the model as needed. However, if the model is larger than your total amount of RAM or if your system is low on available memory, using mmap might increase the risk of pageouts, negatively impacting performance. Disabling mmap results in slower load times but may reduce pageouts if you're not using `--mlock`. Note that if the model is larger than the total amount of RAM, turning off mmap would prevent the model from loading at all.
+-   `-lm MODE, --load-mode MODE`: Specify the model loading mode (default: `auto`).
+    -   `auto`: Memory-map the model, unless the device does not support it.
+    -   `none`: No special loading mode. Disabling mmap results in slower load times but may reduce pageouts if you're not using `mlock`. Note that if the model is larger than the total amount of RAM, turning off mmap would prevent the model from loading at all.
+    -   `mmap`: Memory-map the model.
+    -   `mlock`: Lock the model in memory, preventing it from being swapped out when memory-mapped. This can improve performance but trades away some of the advantages of memory-mapping by requiring more RAM to run and potentially slowing down load times as the model loads into RAM.
+    -   `mmap+mlock`: Memory-map the model and lock it in memory.
+    -   `dio`: Use DirectIO if available.
 
 ### NUMA support
 

tools/llama-bench/README.md

@@ -67,8 +67,8 @@ test parameters:
   -nkvo, --no-kv-offload <0|1>              (default: 0)
   -fa, --flash-attn <on|off|auto>           (default: auto)
   -dev, --device <dev0/dev1/...>            (default: auto)
-  -mmp, --mmap <0|1>                        (default: 1)
-  -dio, --direct-io <0|1>                   (default: 0)
+  -mmp, --mmap <0|1>                        (DEPRECATED IN FAVOUR OF --load-mode)
+  -dio, --direct-io <0|1>                   (DEPRECATED IN FAVOUR OF --load-mode)
   -embd, --embeddings <0|1>                 (default: 0)
   -ts, --tensor-split <ts0/ts1/..>          (default: 0)
   -ot --override-tensor <tensor name pattern>=<buffer type>;...

tools/mtmd/clip-impl.h

@@ -6,6 +6,7 @@
 
 #include <array>
 #include <climits>
+#include <cmath>
 #include <cstdarg>
 #include <cinttypes>
 #include <string>
@@ -603,7 +604,7 @@ struct clip_image_u8 {
             // return a dummy value, so that legacy code can still process image without errors
             return { 0, 0, 0 };
         }
-        int idx = (y * nx + x) * 3;
+        size_t idx = ((size_t) y * (size_t) nx + (size_t) x) * 3;
         return { buf[idx], buf[idx + 1], buf[idx + 2] };
     }
 
@@ -611,8 +612,8 @@ struct clip_image_u8 {
         if (is_placeholder()) {
             return; // no-op
         }
-        int idx = (y * nx + x) * 3;
-        buf[idx] = rgb[0];
+        size_t idx = ((size_t) y * (size_t) nx + (size_t) x) * 3;
+        buf[idx]     = rgb[0];
         buf[idx + 1] = rgb[1];
         buf[idx + 2] = rgb[2];
     }
@@ -642,9 +643,25 @@ struct mtmd_serialization; // forward declaration
 struct clip_image_f32 {
     // marks the global view in e.g., DeepSeek-OCR Models
     bool add_viewsep = false;
-    // whether a learned newline (or EOI) token should be appended after the image (eg Granite4 Vision)
+    // appends a learned newline (or EOI) token after the image
+    // no model uses it now (Granite4 Vision moved to anyres), kept for future models
     bool add_newline = false;
 
+    // llava-next "anyres" tiling, used by Granite4 Vision
+    // the whole grid is encoded and assembled in a single graph
+    // NOTE: excluded from serialized: a deserialized image is always a placeholder, which is never encoded
+    struct anyres_info {
+        int grid_x = 0; // tiles per row, 0 means the image is not tiled
+        int grid_y = 0; // tiles per column
+        int orig_nx = 0; // size of the source image, used to drop the padding tokens
+        int orig_ny = 0;
+
+        bool is_tiled() const {
+            return grid_x > 0 && grid_y > 0;
+        }
+    };
+    anyres_info anyres;
+
     clip_image_size get_size() const {
         return { nx_, ny_ };
     }
@@ -726,6 +743,25 @@ struct clip_image_f32 {
     }
 };
 
+// token area kept after removing the padding added by the anyres resize
+// ref: https://github.com/huggingface/transformers/blob/v5.0.0/src/transformers/models/llava_next/modeling_llava_next.py#L109
+static inline void clip_anyres_unpad(int cur_w, int cur_h, int orig_w, int orig_h,
+                                     int & off_x, int & off_y, int & out_w, int & out_h) {
+    off_x = 0;
+    off_y = 0;
+    out_w = cur_w;
+    out_h = cur_h;
+    if ((float) orig_w / orig_h > (float) cur_w / cur_h) {
+        const int new_h = (int) std::floor((double) orig_h * cur_w / orig_w + 1e-7);
+        off_y = (cur_h - new_h) / 2;
+        out_h = cur_h - 2 * off_y;
+    } else {
+        const int new_w = (int) std::floor((double) orig_w * cur_h / orig_h + 1e-7);
+        off_x = (cur_w - new_w) / 2;
+        out_w = cur_w - 2 * off_x;
+    }
+}
+
 //
 // logging
 //

tools/mtmd/clip.cpp

@@ -1595,6 +1595,9 @@ struct clip_model_loader {
                         hparams.image_resize_algo = RESIZE_ALGO_BICUBIC_PILLOW;
                         hparams.image_resize_pad  = PAD_NONE;
                         get_u32(KEY_SPATIAL_MERGE_SIZE, hparams.n_merge, false);
+                        // n_merge is used as a divisor in clip_image_batch_encode
+                        // (gh / n_merge); reject 0 to avoid int div-by-zero (DoS).
+                        GGML_ASSERT(hparams.n_merge > 0);
                         hparams.rope_theta = 10000.0f; // vision_config.rope_theta
                         // MiniMax-M3: max_pixels 451584 (=672^2) -> 576 merged tokens (image_seq_length)
                         hparams.set_limit_image_tokens(8, 576);
@@ -1823,7 +1826,9 @@ struct clip_model_loader {
                         // unlimited-ocr shares the v1 projector but tiles up to 32
                         get_u32(KEY_PREPROC_MIN_TILES, hparams.preproc_min_tiles, false);
                         get_u32(KEY_PREPROC_MAX_TILES, hparams.preproc_max_tiles, false);
-                        GGML_ASSERT(hparams.preproc_min_tiles <= hparams.preproc_max_tiles);
+                        GGML_ASSERT(hparams.preproc_min_tiles >= 0
+                                    && hparams.preproc_min_tiles <= hparams.preproc_max_tiles
+                                    && hparams.preproc_max_tiles <= 256);
                      } break;
                 case PROJECTOR_TYPE_HUNYUANVL:
                     {
@@ -1888,6 +1893,9 @@ struct clip_model_loader {
                         hparams.audio_window_len       = 400;
                         hparams.audio_hop_len          = 160;
                         get_u32(KEY_A_CHUNK_SIZE,           hparams.audio_chunk_size);
+                        // context_size is squared for the attn_dists/mask buffers; cap to prevent int32 overflow
+                        // (legitimate values are small, e.g. 12-200; 8192^2 = 67M still fits int32)
+                        GGML_ASSERT(hparams.audio_chunk_size > 0 && hparams.audio_chunk_size <= 8192);
                         get_u32(KEY_A_CONV_KERNEL_SIZE,     hparams.audio_conv_kernel_size);
                         get_u32(KEY_A_MAX_POS_EMB,          hparams.audio_max_pos_emb);
                         get_u32(KEY_A_PROJ_WINDOW_SIZE,     hparams.audio_proj_window_size);
@@ -1927,8 +1935,9 @@ struct clip_model_loader {
                     // note: some models having hparams.image_size == 0, which means the image size is dynamic
                     throw std::runtime_error(string_format("%s: image_size (%d) cannot be negative\n", __func__, hparams.image_size));
                 }
-                if (hparams.image_size > 65536) {
-                    throw std::runtime_error(string_format("%s: image_size (%d) is too large (max 65536)\n", __func__, hparams.image_size));
+                if (hparams.image_size > 8192) {
+                    // cap prevents int32 overflow in n_patches = (image_size/patch_size)^2
+                    throw std::runtime_error(string_format("%s: image_size (%d) is too large (max 8192)\n", __func__, hparams.image_size));
                 }
                 if (hparams.patch_size <= 0 || hparams.patch_size >= 65536) {
                     throw std::runtime_error(string_format("%s: patch_size (%d) must be positive and less than 65536\n", __func__, hparams.patch_size));
@@ -1939,9 +1948,12 @@ struct clip_model_loader {
                 if (hparams.image_max_pixels < hparams.image_min_pixels) {
                     throw std::runtime_error(string_format("%s: image_max_pixels (%d) is less than image_min_pixels (%d)\n", __func__, hparams.image_max_pixels, hparams.image_min_pixels));
                 }
-                if (hparams.n_merge < 0 || hparams.n_merge >= 65536) {
+                if (hparams.n_merge <= 0 || hparams.n_merge >= 65536) {
                     throw std::runtime_error(string_format("%s: n_merge (%d) must be greater than 0 and less than 65536\n", __func__, hparams.n_merge));
                 }
+                if (hparams.attn_window_size > 4096) {
+                    throw std::runtime_error(string_format("%s: attn_window_size (%d) is too large (max 4096)\n", __func__, hparams.attn_window_size));
+                }
             }
 
             LOG_INF("%s: projector:          %s\n", __func__, proj_type.c_str());
@@ -3734,6 +3746,9 @@ struct clip_model_loader {
             }
             return;
         }
+        if (gguf_get_kv_type(ctx_gguf.get(), i) != GGUF_TYPE_ARRAY) {
+            throw std::runtime_error(string_format("%s: key '%s' is not an array\n", __func__, key.c_str()));
+        }
         const auto type = gguf_get_arr_type(ctx_gguf.get(), i);
         if (type != GGUF_TYPE_FLOAT32) {
             throw std::runtime_error(string_format("%s: array '%s' has type %d, expected %d (GGUF_TYPE_FLOAT32)\n", __func__, key.c_str(), type, GGUF_TYPE_FLOAT32));
@@ -3768,6 +3783,9 @@ struct clip_model_loader {
             }
             return;
         }
+        if (gguf_get_kv_type(ctx_gguf.get(), i) != GGUF_TYPE_ARRAY) {
+            throw std::runtime_error(string_format("%s: key '%s' is not an array\n", __func__, key.c_str()));
+        }
         const auto type = gguf_get_arr_type(ctx_gguf.get(), i);
         if (type != GGUF_TYPE_INT32) {
             throw std::runtime_error(string_format("%s: array '%s' has type %d, expected %d (GGUF_TYPE_INT32)\n", __func__, key.c_str(), type, GGUF_TYPE_INT32));
@@ -4217,18 +4235,20 @@ int clip_n_output_tokens(const clip_ctx * ctx, const clip_image_f32 * img) {
         case PROJECTOR_TYPE_GRANITE4_VISION:
             {
                 // Per-tile output token count: each projector block outputs
-                // query_side^2 tokens per window × n^2 windows.
-                // For 384×384 input: n = 24/8 = 3, query_side = 4 → 144.
+                // query_side^2 tokens per window x n^2 windows.
+                // For 384x384 input: n = 24/8 = 3, query_side = 4 -> 144.
                 const int window_side = ctx->model.hparams.downsample_window_side;
                 const int query_side  = ctx->model.hparams.downsample_query_side;
                 const int side        = img->nx() / params.patch_size;
                 const int n           = side / window_side;
-                n_patches             = (query_side * n) * (query_side * n);
-                if (img->add_newline) {
-                    // For single-tile case: append 1 newline row.
-                    // For multi-tile rowwise: handled by caller, but here we
-                    // report the per-tile count including one trailing newline.
-                    n_patches += 1;
+                const int out_side    = query_side * n;
+                n_patches             = out_side * out_side;
+                if (img->anyres.is_tiled()) {
+                    // overview tile, then the unpadded tile grid with one newline per row
+                    int off_x, off_y, w, h;
+                    clip_anyres_unpad(img->anyres.grid_x * out_side, img->anyres.grid_y * out_side,
+                                      img->anyres.orig_nx, img->anyres.orig_ny, off_x, off_y, w, h);
+                    n_patches += h * (w + 1);
                 }
             } break;
         default:
@@ -5408,13 +5428,13 @@ bool clip_encode(struct clip_ctx * ctx, struct clip_encode_params * params) {
                 const int context_size = ctx->model.hparams.audio_chunk_size;
                 const int max_pos_emb  = ctx->model.hparams.audio_max_pos_emb;
 
-                std::vector<int32_t> dists(context_size * context_size);
+                std::vector<int32_t> dists((size_t) context_size * (size_t) context_size);
                 for (int i = 0; i < context_size; i++) {
                     for (int j = 0; j < context_size; j++) {
                         int d = i - j;
                         if (d < -context_size) d = -context_size;
                         if (d >  context_size) d =  context_size;
-                        dists[i * context_size + j] = d + max_pos_emb;
+                        dists[(size_t) i * (size_t) context_size + (size_t) j] = d + max_pos_emb;
                     }
                 }
                 set_input_i32("attn_dists", dists);
@@ -5423,13 +5443,13 @@ bool clip_encode(struct clip_ctx * ctx, struct clip_encode_params * params) {
                 const int remainder  = n_frames % context_size;
                 if (remainder > 0) {
                     const int num_blocks = (n_frames + context_size - 1) / context_size;
-                    std::vector<float> mask(context_size * context_size * num_blocks, 0.0f);
+                    std::vector<float> mask((size_t) context_size * (size_t) context_size * (size_t) num_blocks, 0.0f);
                     const float neg_inf = -INFINITY;
-                    const int last_block_offset = (num_blocks - 1) * context_size * context_size;
+                    const size_t last_block_offset = (size_t) (num_blocks - 1) * (size_t) context_size * (size_t) context_size;
                     for (int q = 0; q < context_size; q++) {
                         for (int k = 0; k < context_size; k++) {
                             if (q >= remainder || k >= remainder) {
-                                mask[last_block_offset + q * context_size + k] = neg_inf;
+                                mask[last_block_offset + (size_t) q * (size_t) context_size + (size_t) k] = neg_inf;
                             }
                         }
                     }
@@ -5493,10 +5513,18 @@ bool clip_encode(struct clip_ctx * ctx, struct clip_encode_params * params) {
                     return idx;
                 };
 
+                // the same permutation is applied to every tile of the stacked image
                 auto upload = [&](const std::string & name, const std::vector<int32_t> & idx) {
                     ggml_tensor * t = ggml_graph_get_tensor(gf, name.c_str());
                     GGML_ASSERT(t);
-                    ggml_backend_tensor_set(t, idx.data(), 0, idx.size() * sizeof(int32_t));
+                    GGML_ASSERT(ggml_nelements(t) % (int64_t) idx.size() == 0);
+                    const int n_rep = ggml_nelements(t) / idx.size();
+                    std::vector<int32_t> buf;
+                    buf.reserve(idx.size() * n_rep);
+                    for (int i = 0; i < n_rep; ++i) {
+                        buf.insert(buf.end(), idx.begin(), idx.end());
+                    }
+                    ggml_backend_tensor_set(t, buf.data(), 0, ggml_nbytes(t));
                 };
 
                 // Stage 1b only uses block 0's permutations; future stages

tools/mtmd/models/granite4-vision.cpp

@@ -14,18 +14,39 @@
  *   Stage 1a: SigLIP vision tower (N layers, post-norm)
  *   Stage 1b: WindowQFormer blocks (deepstack + spatial)
  *   Stage 1c: Concatenate and pack outputs
- *   Stage 1d: Append newline tokens if add_newline is set
+ *   Stage 1d: Assemble the anyres tiles into one token sequence
  */
 
 // ---------------------------------------------------------------------------
 // Member method implementations
 // ---------------------------------------------------------------------------
 
+// split the stacked tiles into the batch axis, then run the usual patch embedding
+ggml_tensor * clip_graph_granite4_vision::build_tile_inp() {
+    ggml_tensor * inp_raw = build_inp_raw();
+
+    if (n_tiles > 1) {
+        const int px = img.nx();
+        inp_raw = ggml_reshape_4d(ctx0, inp_raw, px * px, n_tiles, 3, 1);
+        inp_raw = ggml_cont(ctx0, ggml_permute(ctx0, inp_raw, 0, 2, 1, 3));
+        inp_raw = ggml_reshape_4d(ctx0, inp_raw, px, px, 3, n_tiles);
+    }
+
+    ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings_0, inp_raw, patch_size, patch_size, 0, 0, 1, 1);
+    inp = ggml_reshape_3d(ctx0, inp, tile_side * tile_side, n_embd, n_tiles);
+    inp = ggml_cont(ctx0, ggml_transpose(ctx0, inp));
+    if (model.patch_bias) {
+        inp = ggml_add(ctx0, inp, model.patch_bias);
+    }
+    return inp;
+}
+
 ggml_tensor * clip_graph_granite4_vision::gather(
         ggml_tensor * src,
         const std::string & name,
         int idx_len) {
-    ggml_tensor * idx = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, idx_len);
+    // one index row per tile, all rows hold the same permutation
+    ggml_tensor * idx = ggml_new_tensor_2d(ctx0, GGML_TYPE_I32, idx_len, n_tiles);
     ggml_set_name(idx, name.c_str());
     ggml_set_input(idx);
     return ggml_get_rows(ctx0, src, idx);
@@ -36,12 +57,15 @@ ggml_tensor * clip_graph_granite4_vision::interp_down(
         int side,
         int new_side) {
     const int n_embd = src->ne[0];
-    ggml_tensor * t = ggml_reshape_4d(ctx0, src, n_embd, side, side, 1);
+    ggml_tensor * t = ggml_reshape_4d(ctx0, src, n_embd, side, side, n_tiles);
     t = ggml_cont(ctx0, ggml_permute(ctx0, t, 2, 0, 1, 3));
+    // fold the tile axis into the channel axis, ggml_pool_2d only pools the first two axes
+    t = ggml_reshape_3d(ctx0, t, side, side, n_embd * n_tiles);
     const int kernel = side / new_side;
     t = ggml_pool_2d(ctx0, t, GGML_OP_POOL_AVG, kernel, kernel, kernel, kernel, 0, 0);
+    t = ggml_reshape_4d(ctx0, t, new_side, new_side, n_embd, n_tiles);
     t = ggml_cont(ctx0, ggml_permute(ctx0, t, 1, 2, 0, 3));
-    return ggml_reshape_2d(ctx0, t, n_embd, new_side * new_side);
+    return ggml_reshape_3d(ctx0, t, n_embd, new_side * new_side, n_tiles);
 }
 
 // ---------------------------------------------------------------------------
@@ -63,6 +87,7 @@ ggml_tensor * clip_graph_granite4_vision::build_block(
     const int n = image_side / window_side;
     const int new_side = n * query_side;
     const int n_windows = n * n;
+    const int n_win_all = n_windows * n_tiles; // windows of every tile, batched together
     const int enc_len = window_side * window_side;
     const int query_len = query_side * query_side;
 
@@ -82,7 +107,7 @@ ggml_tensor * clip_graph_granite4_vision::build_block(
         ggml_tensor * enc_flat = gather(x,
             "g4v_blk" + std::to_string(bid) + "_win_idx",
             image_side * image_side);
-        enc = ggml_reshape_3d(ctx0, enc_flat, n_embd, enc_len, n_windows);
+        enc = ggml_reshape_3d(ctx0, enc_flat, n_embd, enc_len, n_win_all);
     }
     cbx(enc, "enc");
 
@@ -104,7 +129,7 @@ ggml_tensor * clip_graph_granite4_vision::build_block(
         ggml_tensor * dw_flat = gather(d,
             "g4v_blk" + std::to_string(bid) + "_qwin_idx",
             new_side * new_side);
-        ggml_tensor * dw = ggml_reshape_3d(ctx0, dw_flat, n_embd, query_len, n_windows);
+        ggml_tensor * dw = ggml_reshape_3d(ctx0, dw_flat, n_embd, query_len, n_win_all);
         q_in = ggml_add(ctx0, dw, blk.qf_proj_query);
     }
     cbx(q_in, "query_embeds");
@@ -140,12 +165,12 @@ ggml_tensor * clip_graph_granite4_vision::build_block(
         ggml_tensor * K = linear(q, pl.k_w, pl.k_b);
         ggml_tensor * V = linear(q, pl.v_w, pl.v_b);
 
-        Q = ggml_reshape_4d(ctx0, Q, d_h, n_head, nq, n_windows);
-        K = ggml_reshape_4d(ctx0, K, d_h, n_head, nq, n_windows);
-        V = ggml_reshape_4d(ctx0, V, d_h, n_head, nq, n_windows);
+        Q = ggml_reshape_4d(ctx0, Q, d_h, n_head, nq, n_win_all);
+        K = ggml_reshape_4d(ctx0, K, d_h, n_head, nq, n_win_all);
+        V = ggml_reshape_4d(ctx0, V, d_h, n_head, nq, n_win_all);
 
         sa_out = build_attn(pl.o_w, pl.o_b, Q, K, V, nullptr, scale, bid);
-        sa_out = ggml_reshape_3d(ctx0, sa_out, n_embd, nq, n_windows);
+        sa_out = ggml_reshape_3d(ctx0, sa_out, n_embd, nq, n_win_all);
 
         sa_out = ggml_add(ctx0, sa_out, q);
         sa_out = build_norm(sa_out, pl.ln_1_w, pl.ln_1_b,
@@ -166,13 +191,13 @@ ggml_tensor * clip_graph_granite4_vision::build_block(
         ggml_tensor * K = linear(e_in, pl.cross_attn_k_w, pl.cross_attn_k_b);
         ggml_tensor * V = linear(e_in, pl.cross_attn_v_w, pl.cross_attn_v_b);
 
-        Q = ggml_reshape_4d(ctx0, Q, d_h, n_head, nq, n_windows);
-        K = ggml_reshape_4d(ctx0, K, d_h, n_head, nkv, n_windows);
-        V = ggml_reshape_4d(ctx0, V, d_h, n_head, nkv, n_windows);
+        Q = ggml_reshape_4d(ctx0, Q, d_h, n_head, nq, n_win_all);
+        K = ggml_reshape_4d(ctx0, K, d_h, n_head, nkv, n_win_all);
+        V = ggml_reshape_4d(ctx0, V, d_h, n_head, nkv, n_win_all);
 
         ca_out = build_attn(pl.cross_attn_o_w, pl.cross_attn_o_b,
                             Q, K, V, nullptr, scale, bid);
-        ca_out = ggml_reshape_3d(ctx0, ca_out, n_embd, nq, n_windows);
+        ca_out = ggml_reshape_3d(ctx0, ca_out, n_embd, nq, n_win_all);
 
         ca_out = ggml_add(ctx0, ca_out, sa_out);
         ca_out = build_norm(ca_out, pl.cross_attn_norm_w, pl.cross_attn_norm_b,
@@ -183,13 +208,13 @@ ggml_tensor * clip_graph_granite4_vision::build_block(
     // 6c. FFN
     ggml_tensor * ffn;
     {
-        ggml_tensor * t = ggml_reshape_2d(ctx0, ca_out, n_embd, query_len * n_windows);
+        ggml_tensor * t = ggml_reshape_2d(ctx0, ca_out, n_embd, query_len * n_win_all);
         t = build_mm(pl.ff_up_w, t);
         if (pl.ff_up_b) t = ggml_add(ctx0, t, pl.ff_up_b);
         t = ggml_gelu_erf(ctx0, t);
         t = build_mm(pl.ff_down_w, t);
         if (pl.ff_down_b) t = ggml_add(ctx0, t, pl.ff_down_b);
-        t = ggml_reshape_3d(ctx0, t, n_embd, query_len, n_windows);
+        t = ggml_reshape_3d(ctx0, t, n_embd, query_len, n_win_all);
         ffn = ggml_add(ctx0, t, ca_out);
         ffn = build_norm(ffn, pl.ln_2_w, pl.ln_2_b, NORM_TYPE_NORMAL, qformer_eps, bid);
     }
@@ -198,7 +223,7 @@ ggml_tensor * clip_graph_granite4_vision::build_block(
     // 7. _unwin back to raster
     ggml_tensor * unwinned;
     {
-        ggml_tensor * flat = ggml_reshape_2d(ctx0, ffn, n_embd, query_len * n_windows);
+        ggml_tensor * flat = ggml_reshape_3d(ctx0, ffn, n_embd, query_len * n_windows, n_tiles);
         unwinned = gather(flat,
             "g4v_blk" + std::to_string(bid) + "_unwin_idx",
             new_side * new_side);
@@ -244,13 +269,42 @@ ggml_tensor * clip_graph_granite4_vision::build_newline_row(ggml_context * ctx0)
     return ggml_reshape_2d(ctx0, nl_row_2d, n_mmproj_embd, 1);
 }
 
-// Append a single newline row at the end of the tile output.
-ggml_tensor * clip_graph_granite4_vision::append_rowwise_newlines(ggml_context * ctx0, ggml_tensor * tile_output) {
-    // For the single-tile case, append one newline row at the end.
-    // For the multi-tile rowwise case, this will be called per-tile
-    // (though currently only the single-tile path uses it).
-    ggml_tensor * nl_row = build_newline_row(ctx0);
-    return ggml_concat(ctx0, tile_output, nl_row, 1);
+// Assemble [overview, tile(0,0), tile(0,1), ...] into one token sequence:
+// the overview tokens first, then the tile grid read in raster order with one newline per row.
+// ref: https://github.com/huggingface/transformers/blob/v5.0.0/src/transformers/models/llava_next/modeling_llava_next.py#L266
+ggml_tensor * clip_graph_granite4_vision::build_anyres_assembly(ggml_tensor * cur, int out_side) {
+    const int n_dim  = cur->ne[0];
+    const int grid_x = anyres.grid_x;
+    const int grid_y = anyres.grid_y;
+    const int cur_w  = grid_x * out_side;
+    const int cur_h  = grid_y * out_side;
+    GGML_ASSERT(cur->ne[1] == out_side * out_side);
+    GGML_ASSERT(cur->ne[2] == 1 + grid_x * grid_y);
+
+    ggml_tensor * base = ggml_view_2d(ctx0, cur, n_dim, out_side * out_side, cur->nb[1], 0);
+
+    ggml_tensor * tiles = ggml_view_3d(ctx0, cur, n_dim, out_side * out_side, grid_x * grid_y,
+                                       cur->nb[1], cur->nb[2], cur->nb[2]);
+
+    // (n_dim*out_side, out_side, grid_x, grid_y) -> interleave the tiles of a grid row
+    tiles = ggml_reshape_4d(ctx0, tiles, n_dim * out_side, out_side, grid_x, grid_y);
+    tiles = ggml_cont(ctx0, ggml_permute(ctx0, tiles, 0, 2, 1, 3));
+    tiles = ggml_reshape_3d(ctx0, tiles, n_dim, cur_w, cur_h);
+
+    // drop the tokens that only cover the padding added when resizing to the grid
+    int off_x, off_y, w, h;
+    clip_anyres_unpad(cur_w, cur_h, anyres.orig_nx, anyres.orig_ny, off_x, off_y, w, h);
+    if (w != cur_w || h != cur_h) {
+        tiles = ggml_cont(ctx0, ggml_view_3d(ctx0, tiles, n_dim, w, h,
+                                             tiles->nb[1], tiles->nb[2],
+                                             off_x * tiles->nb[1] + off_y * tiles->nb[2]));
+    }
+
+    ggml_tensor * nl = ggml_repeat_4d(ctx0, build_newline_row(ctx0), n_dim, 1, h, 1);
+    tiles = ggml_concat(ctx0, tiles, nl, 1);
+    tiles = ggml_reshape_2d(ctx0, tiles, n_dim, (w + 1) * h);
+
+    return ggml_concat(ctx0, base, tiles, 1);
 }
 
 ggml_cgraph * clip_graph_granite4_vision::build() {
@@ -260,10 +314,12 @@ ggml_cgraph * clip_graph_granite4_vision::build() {
     GGML_ASSERT(!model.qf_proj_blocks.empty());
 
     // --- Stage 1a: SigLIP encoder producing intermediate hidden states ---
-    ggml_tensor * inp = build_inp();
+    ggml_tensor * inp = build_tile_inp();
     inp = ggml_add(ctx0, inp, model.position_embeddings);
     cb(inp, "pos_embed", -1);
 
+    const int tile_n_patches = tile_side * tile_side;
+
     ggml_tensor * inpL = inp;
     std::vector<ggml_tensor *> layer_outs(n_layer, nullptr);
 
@@ -281,12 +337,13 @@ ggml_cgraph * clip_graph_granite4_vision::build() {
         ggml_tensor * Vcur = build_mm(layer.v_w, cur);
         if (layer.v_b) Vcur = ggml_add(ctx0, Vcur, layer.v_b);
 
-        Qcur = ggml_reshape_3d(ctx0, Qcur, d_head, n_head, n_patches);
-        Kcur = ggml_reshape_3d(ctx0, Kcur, d_head, n_head, n_patches);
-        Vcur = ggml_reshape_3d(ctx0, Vcur, d_head, n_head, n_patches);
+        Qcur = ggml_reshape_4d(ctx0, Qcur, d_head, n_head, tile_n_patches, n_tiles);
+        Kcur = ggml_reshape_4d(ctx0, Kcur, d_head, n_head, tile_n_patches, n_tiles);
+        Vcur = ggml_reshape_4d(ctx0, Vcur, d_head, n_head, tile_n_patches, n_tiles);
 
         cur = build_attn(layer.o_w, layer.o_b,
                          Qcur, Kcur, Vcur, nullptr, kq_scale, il);
+        cur = ggml_reshape_3d(ctx0, cur, n_embd, tile_n_patches, n_tiles);
 
         cur = ggml_add(ctx0, cur, inpL);
         inpL = cur;
@@ -318,7 +375,7 @@ ggml_cgraph * clip_graph_granite4_vision::build() {
         ggml_tensor * stream = build_block(
             blk, h, bid,
             hparams.proj_spatial_offsets[bid],
-            n_patches_x,
+            tile_side,
             hparams.downsample_window_side,
             hparams.downsample_query_side,
             qformer_eps);
@@ -326,10 +383,11 @@ ggml_cgraph * clip_graph_granite4_vision::build() {
         mmproj = mmproj ? ggml_concat(ctx0, mmproj, stream, 0) : stream;
     }
 
-    // --- Stage 1d: Append newline tokens if add_newline is set ---
-    if (add_newline) {
-        mmproj = append_rowwise_newlines(ctx0, mmproj);
-        ggml_set_name(mmproj, "g4v_mmproj_out_nl");
+    // --- Stage 1d: assemble the tiles and weave in the newline tokens ---
+    if (anyres.is_tiled()) {
+        const int out_side = tile_side / hparams.downsample_window_side * hparams.downsample_query_side;
+        mmproj = build_anyres_assembly(mmproj, out_side);
+        ggml_set_name(mmproj, "g4v_mmproj_out_anyres");
     } else {
         ggml_set_name(mmproj, "g4v_mmproj_out");
     }

tools/mtmd/models/models.h

@@ -402,16 +402,19 @@ struct clip_graph_exaone4_5 : clip_graph {
 struct clip_graph_granite4_vision : clip_graph {
     clip_graph_granite4_vision(clip_ctx * ctx, const clip_image_f32 & img)
         : clip_graph(ctx, img),
-          add_newline(img.add_newline) {}
+          anyres(img.anyres),
+          n_tiles(img.ny() / img.nx()),
+          tile_side(img.nx() / patch_size) {}
 
     ggml_cgraph * build() override;
 
 private:
-    // The graph is per-tile since only batch-size 1 is supported in clip. As
-    // such, this value is set at construct time based on the tile that will be
-    // encoded, then used during build to determine how to handle newlines.
-    const bool add_newline;
+    // the input image is a stack of tiles on the Y axis: [overview, tile(0,0), tile(0,1), ...]
+    const clip_image_f32::anyres_info anyres;
+    const int n_tiles;
+    const int tile_side; // patches per tile side
 
+    ggml_tensor * build_tile_inp();
     ggml_tensor * gather(ggml_tensor * src, const std::string & name, int idx_len);
     ggml_tensor * interp_down(ggml_tensor * src, int side, int new_side);
     ggml_tensor * build_block(const qf_block & blk, ggml_tensor * h, int bid,
@@ -419,7 +422,7 @@ private:
                               int query_side, float qformer_eps);
 
     ggml_tensor * build_newline_row(ggml_context * ctx0);
-    ggml_tensor * append_rowwise_newlines(ggml_context * ctx0, ggml_tensor * tile_output);
+    ggml_tensor * build_anyres_assembly(ggml_tensor * cur, int out_side);
 };
 
 struct clip_graph_muse_glimmer : clip_graph {

tools/mtmd/mtmd-helper-common.h

@@ -82,7 +82,7 @@ struct decode_embd_batch {
     llama_batch batch;
     decode_embd_batch(float * embd, int32_t n_tokens, int n_pos_per_embd, int n_mmproj_embd) : n_pos_per_embd(n_pos_per_embd), n_mmproj_embd(n_mmproj_embd) {
         GGML_ASSERT(n_tokens > 0 && n_pos_per_embd > 0 && n_mmproj_embd > 0);
-        pos     .resize(n_tokens * n_pos_per_embd);
+        pos     .resize((size_t) n_tokens * (size_t) n_pos_per_embd);
         n_seq_id.resize(n_tokens);
         seq_ids .resize(n_tokens + 1);
         logits  .resize(n_tokens);
@@ -115,10 +115,12 @@ struct decode_embd_batch {
         GGML_ASSERT(!rel_pos.empty() && (int32_t)rel_pos.size() == batch.n_tokens);
         seq_id_0[0] = seq_id;
         for (int32_t i = 0; i < batch.n_tokens; i++) {
-            pos[i                     ] = rel_pos[i].t;
-            pos[i + batch.n_tokens    ] = rel_pos[i].y;
-            pos[i + batch.n_tokens * 2] = rel_pos[i].x;
-            pos[i + batch.n_tokens * 3] = rel_pos[i].z;
+            const size_t idx = (size_t) i;
+            const size_t n_tokens = (size_t) batch.n_tokens;
+            pos[idx                    ] = rel_pos[i].t;
+            pos[idx + n_tokens         ] = rel_pos[i].y;
+            pos[idx + n_tokens * 2     ] = rel_pos[i].x;
+            pos[idx + n_tokens * 3     ] = rel_pos[i].z;
         }
         for (int i = 0; i < batch.n_tokens; i++) {
             batch.n_seq_id[i] = 1;
@@ -132,10 +134,12 @@ struct decode_embd_batch {
         GGML_ASSERT(n_pos_per_embd == 4);
         seq_id_0[0] = seq_id;
         for (int i = 0; i < batch.n_tokens; i++) {
-            pos[i                     ] = pos_0 + i;
-            pos[i + batch.n_tokens    ] = pos_0 + i;
-            pos[i + batch.n_tokens * 2] = pos_0 + i;
-            pos[i + batch.n_tokens * 3] = pos_0 + i;
+            const size_t idx = (size_t) i;
+            const size_t n_tokens = (size_t) batch.n_tokens;
+            pos[idx                    ] = pos_0 + i;
+            pos[idx + n_tokens         ] = pos_0 + i;
+            pos[idx + n_tokens * 2     ] = pos_0 + i;
+            pos[idx + n_tokens * 3     ] = pos_0 + i;
         }
         for (int i = 0; i < batch.n_tokens; i++) {
             batch.n_seq_id[i] = 1;
@@ -148,7 +152,7 @@ struct decode_embd_batch {
         GGML_ASSERT(offset >= 0 && n_tokens > 0 && offset + n_tokens <= batch.n_tokens);
         llama_pos * pos_ptr;
         pos_view.clear();
-        pos_view.reserve(n_tokens * n_pos_per_embd);
+        pos_view.reserve((size_t) n_tokens * (size_t) n_pos_per_embd);
         if (n_pos_per_embd > 1) {
             // mrope
             // for example, with layout of src: 1234...1234...1234...1234...
@@ -157,7 +161,7 @@ struct decode_embd_batch {
                 // assume n_tokens is less than or equal to batch.n_tokens
                 // batch.n_tokens is number of **total** tokens
                 // n_tokens is number of viewed token
-                size_t src_idx = i * batch.n_tokens + offset;
+                size_t src_idx = (size_t) i * (size_t) batch.n_tokens + (size_t) offset;
                 pos_view.insert(pos_view.end(),
                     pos.data() + src_idx,
                     pos.data() + src_idx + n_tokens);

tools/mtmd/mtmd-image.cpp

@@ -1317,7 +1317,7 @@ void mtmd_image_preprocessor_step3vl::img_u8_resize_bilinear_to_f32(
     const float scale_x = static_cast<float>(src_size.width)  / target_width;
     const float scale_y = static_cast<float>(src_size.height) / target_height;
 
-    std::vector<float> local_buf(3 * target_width * target_height);
+    std::vector<float> local_buf((size_t) 3 * (size_t) target_width * (size_t) target_height);
 
     for (int y = 0; y < target_height; ++y) {
         const float src_y = (static_cast<float>(y) + 0.5f) * scale_y - 0.5f;
@@ -1338,7 +1338,7 @@ void mtmd_image_preprocessor_step3vl::img_u8_resize_bilinear_to_f32(
             const auto p10 = src.get_pixel(x0, y1);
             const auto p11 = src.get_pixel(x1, y1);
 
-            const size_t idx_dst = 3 * (y * target_width + x);
+            const size_t idx_dst = (size_t) 3 * ((size_t) y * (size_t) target_width + (size_t) x);
             for (int c = 0; c < 3; ++c) {
                 const float v00 = (static_cast<float>(p00[c]) / 255.0f - mean[c]) / std[c];
                 const float v01 = (static_cast<float>(p01[c]) / 255.0f - mean[c]) / std[c];
@@ -1602,17 +1602,50 @@ mtmd_image_preproc_out mtmd_image_preprocessor_youtuvl::preprocess(const clip_im
 }
 
 mtmd_image_preproc_out mtmd_image_preprocessor_granite::preprocess(const clip_image_u8 & img) {
-    auto output = mtmd_image_preprocessor_llava_uhd::preprocess(img);
-    if (output.entries.size() == 0) {
-        // Single-tile (overview only): append one newline row.
-        output.overview.add_newline = true;
-    } else {
-        // Multi-tile: overview gets no newline, grid tiles get one.
-        output.overview.add_newline = false;
-        for (size_t i = 0; i < output.entries.size(); ++i) {
-            output.entries[i].add_newline = true;
+    GGML_ASSERT(!hparams.image_res_candidates.empty());
+
+    const clip_image_size orig_size = img.get_size();
+    const int             tile_size = hparams.image_size;
+
+    // llava-next always encodes an overview plus a grid of tiles, even for small images
+    const clip_image_size refined_size = select_best_resolution(orig_size, hparams.image_res_candidates);
+    const int             grid_x       = refined_size.width  / tile_size;
+    const int             grid_y       = refined_size.height / tile_size;
+
+    clip_image_u8 overview;
+    img_tool::resize(img, overview, {tile_size, tile_size}, hparams.image_resize_algo_ov,
+                        hparams.image_pad_ov, hparams.image_pad_color_ov);
+
+    clip_image_u8 refined;
+    img_tool::resize(img, refined, refined_size, hparams.image_resize_algo_rf,
+                        hparams.image_pad_rf, hparams.image_pad_color_rf);
+
+    // stack the overview and the tiles on the Y axis, so the whole grid goes through one graph
+    clip_image_u8 stacked;
+    stacked.set_size({tile_size, tile_size * (1 + grid_x * grid_y)}, false);
+    auto copy_tile = [&](const clip_image_u8 & src, int src_x, int src_y, int dst_idx) {
+        for (int py = 0; py < tile_size; py++) {
+            for (int px = 0; px < tile_size; px++) {
+                stacked.set_pixel(px, dst_idx * tile_size + py, src.get_pixel(src_x + px, src_y + py));
+            }
+        }
+    };
+    copy_tile(overview, 0, 0, 0);
+    for (int ty = 0; ty < grid_y; ty++) {
+        for (int tx = 0; tx < grid_x; tx++) {
+            copy_tile(refined, tx * tile_size, ty * tile_size, 1 + ty * grid_x + tx);
         }
     }
+
+    LOG_DBG("%s: grid size: %d x %d (%d tiles) + overview\n", __func__, grid_x, grid_y, grid_x * grid_y);
+
+    mtmd_image_preproc_out output;
+    output.append(hparams, stacked, true);
+    auto & entry = output.entries.back();
+    entry.anyres.grid_x  = grid_x;
+    entry.anyres.grid_y  = grid_y;
+    entry.anyres.orig_nx = orig_size.width;
+    entry.anyres.orig_ny = orig_size.height;
     return output;
 }
 

tools/mtmd/mtmd-image.h

@@ -85,9 +85,6 @@ struct mtmd_image_preprocessor_llava_uhd : mtmd_image_preprocessor {
 protected:
     clip_image_size get_best_resize(const clip_image_size & original_size, int scale_resolution, int patch_size, bool allow_upscale = false);
 
-private:
-    clip_image_size resize_maintain_aspect_ratio(const clip_image_size & orig, const clip_image_size & target_max);
-
     /**
      * Selects the best resolution from a list of possible resolutions based on the original size.
      *
@@ -104,6 +101,9 @@ private:
      * @return The best fit resolution
      */
     clip_image_size select_best_resolution(const clip_image_size & original_size, const std::vector<clip_image_size> & possible_resolutions);
+
+private:
+    clip_image_size resize_maintain_aspect_ratio(const clip_image_size & orig, const clip_image_size & target_max);
     int ensure_divide(int length, int patch_size);
     clip_image_size get_refine_size(const clip_image_size & original_size, const clip_image_size & grid, int scale_resolution, int patch_size, bool allow_upscale = false);
     clip_image_size get_best_grid(const int max_slice_nums, const int multiple, const float log_ratio);
@@ -225,7 +225,7 @@ struct mtmd_image_preprocessor_youtuvl : mtmd_image_preprocessor {
     mtmd_image_preproc_out preprocess(const clip_image_u8 & img) override;
 };
 
-// similar to llava_uhd, but has add_newline
+// llava-next "anyres": stacks the overview and all tiles into one image, assembled by clip in a single graph
 struct mtmd_image_preprocessor_granite : mtmd_image_preprocessor_llava_uhd {
     mtmd_image_preprocessor_granite(const clip_ctx * ctx) : mtmd_image_preprocessor_llava_uhd(ctx) {}
     mtmd_image_preproc_out preprocess(const clip_image_u8 & img) override;

tools/mtmd/mtmd.cpp

@@ -891,10 +891,10 @@ struct mtmd_context {
                 } break;
             case PROJECTOR_TYPE_GRANITE4_VISION:
                 {
-                    img_beg = "<image>";
-                    img_end = "";
+                    // ... (image embeddings) \n ...
+                    img_beg = "";
+                    img_end = "\n";
                     image_preproc = std::make_unique<mtmd_image_preprocessor_granite>(ctx_v);
-                    ov_img_first = true;
                 } break;
             default:
                 throw std::runtime_error(string_format("%s: unexpected vision projector type %d\n", __func__, proj));

tools/server/README.md

@@ -226,6 +226,7 @@ For the full list of features, please refer to [server's changelog](https://gith
 | `--jinja, --no-jinja` | whether to use jinja template engine for chat (default: enabled)<br/>(env: LLAMA_ARG_JINJA) |
 | `--reasoning-format FORMAT` | controls whether thought tags are allowed and/or extracted from the response, and in which format they're returned; one of:<br/>- none: leaves thoughts unparsed in `message.content`<br/>- deepseek: puts thoughts in `message.reasoning_content`<br/>- deepseek-legacy: keeps `<think>` tags in `message.content` while also populating `message.reasoning_content`<br/>(default: auto)<br/>(env: LLAMA_ARG_THINK) |
 | `-rea, --reasoning [on\|off\|auto]` | Use reasoning/thinking in the chat ('on', 'off', or 'auto', default: 'auto' (detect from template))<br/>(env: LLAMA_ARG_REASONING) |
+| `--reasoning-effort LEVEL` | reasoning effort level given to the chat template: 'default' to keep the template default,<br/>or a level such as 'minimal', 'low', 'medium', 'high', 'xhigh' or 'max' (default: default)<br/>(env: LLAMA_ARG_REASONING_EFFORT) |
 | `--reasoning-budget N` | token budget for thinking: -1 for unrestricted, 0 for immediate end, N>0 for token budget (default: -1)<br/>(env: LLAMA_ARG_THINK_BUDGET) |
 | `--reasoning-budget-message MESSAGE` | message injected before the end-of-thinking tag when reasoning budget is exhausted (default: none)<br/>(env: LLAMA_ARG_THINK_BUDGET_MESSAGE) |
 | `--reasoning-preserve, --no-reasoning-preserve` | preserve reasoning trace in the full history, not just the last assistant message (default: template default)<br/>compatible with certain templates having 'supports_preserve_reasoning' capability<br/>example: https://docs.z.ai/guides/capabilities/thinking-mode#preserved-thinking<br/>(env: LLAMA_ARG_REASONING_PRESERVE) |
@@ -295,10 +296,17 @@ For the full list of features, please refer to [server's changelog](https://gith
 
 Note: If both command line argument and environment variable are both set for the same param, the argument will take precedence over env var.
 
-For boolean options like `--mmap` or `--kv-offload`, the environment variable is handled as shown in this example:
-- `LLAMA_ARG_MMAP=true` means enabled, other accepted values are: `1`, `on`, `enabled`
-- `LLAMA_ARG_MMAP=false` means disabled, other accepted values are: `0`, `off`, `disabled`
-- If `LLAMA_ARG_NO_MMAP` is present (no matter the value), it means disabling mmap
+For string options like `--load-mode`, the environment variable is handled as shown in this example:
+- `LLAMA_ARG_LOAD_MODE=auto` sets the loading mode to auto (default)
+- `LLAMA_ARG_LOAD_MODE=none` disables special loading
+- `LLAMA_ARG_LOAD_MODE=mmap` enables memory-mapping
+- `LLAMA_ARG_LOAD_MODE=mlock` locks the model in RAM
+- `LLAMA_ARG_LOAD_MODE=mmap+mlock` enables memory-mapping and locks in RAM
+- `LLAMA_ARG_LOAD_MODE=dio` uses DirectIO if available
+
+For boolean options like `--kv-offload`:
+- `LLAMA_ARG_KV_OFFLOAD=true` means enabled, other accepted values are: `1`, `on`, `enabled`
+- `LLAMA_ARG_KV_OFFLOAD=false` means disabled, other accepted values are: `0`, `off`, `disabled`
 
 Example usage of docker compose with environment variables:
 
@@ -1250,7 +1258,7 @@ The `response_format` parameter supports both plain JSON output (e.g. `{"type":
 
 `chat_template_kwargs`: Allows sending additional parameters to the json templating system. For example: `{"enable_thinking": false}`
 
-`reasoning_effort`: If set to `none`, reasoning will be disabled for this request. Other values (e.g., `low`, `max`) have no effect on reasoning.
+`reasoning_effort`: If `none`, reasoning/thinking is disabled. Otherwise, the value is made available to the jinja template.
 
 `reasoning_format`: The reasoning format to be parsed. If set to `none`, it will output the raw generated text.
 
@@ -1892,7 +1900,7 @@ Example events:
 }
 // note for "loading" status:
 // - subsequent events will follow the same order of "stages" list
-// - mmap is may report incorrect progress on some platforms; if you need exact progress, use --no-mmap
+// - mmap may report incorrect progress on some platforms; if you need exact progress, use --load-mode none
 
 {
   "model": "...",

tools/server/server-common.cpp

@@ -1292,12 +1292,15 @@ json oaicompat_chat_params_parse(
         throw std::invalid_argument("invalid type for \"enable_thinking\" (expected boolean, got string)");
     }
 
-    // Parse also the OAI "reasoning_effort": "none" specific value
+    // Parse the OAI "reasoning_effort" field; "none" disables reasoning.
     if (body.contains("reasoning_effort")) {
         auto reasoning_effort = json_value(body, "reasoning_effort", std::string(""));
         if (reasoning_effort == "none") {
             inputs.enable_thinking = false;
-        } // other reasoning_effort values are model-specific and not yet handled
+            inputs.chat_template_kwargs.erase("reasoning_effort");
+        } else if (!reasoning_effort.empty()) {
+            inputs.chat_template_kwargs["reasoning_effort"] = json(reasoning_effort).dump();
+        }
     }
 
     inputs.force_pure_content = opt.force_pure_content;

tools/server/server-context.cpp

@@ -2947,8 +2947,10 @@ private:
         });
 
         // generate the actual drafts (if any)
-        {
-            common_speculative_draft(spec.get());
+        if (!drafting.empty()) {
+            queue_tasks.yield_to_queue([&]() {
+                common_speculative_draft(spec.get());
+            });
         }
 
         // make checkpoints if needed
@@ -3578,8 +3580,8 @@ private:
             has_output |= batch.tokens[i].output;
         }
 
-        // decode on the worker thread, so we can still handle metrics tasks while waiting
-        // note: the sync is done here too, so that the wait also happens off the main thread
+        // yield to the queue, so we can still handle metrics tasks while decoding
+        // note: the sync is done here too, so that the wait is also covered by the yield
         int ret = 0;
         queue_tasks.yield_to_queue([&]() {
             ret = llama_decode(ctx_tgt, batch_view);
@@ -3644,11 +3646,18 @@ private:
         // TODO: avoid restoring the draft context and re-evaluating the drafted tokens when not needed [TAG_SPEC_AVOID_DRAFT_REEVAL]
         //       for now, always re-evaluate for simplicity
         //       ref: https://github.com/ggml-org/llama.cpp/pull/22728#issuecomment-4400925384
-        if (!common_speculative_process(spec.get(), batch_view)) {
-            SRV_ERR("%s", "failed to process speculative batch\n");
+        if (spec) {
+            bool ok = true;
+            queue_tasks.yield_to_queue([&]() {
+                ok = common_speculative_process(spec.get(), batch_view);
+            });
 
-            // TODO: handle error
-            throw std::runtime_error("failed to process speculative batch");
+            if (!ok) {
+                SRV_ERR("%s", "failed to process speculative batch\n");
+
+                // TODO: handle error
+                throw std::runtime_error("failed to process speculative batch");
+            }
         }
 
         // handle `n_cmpl > 1` tasks - when the main prompt is processed, activate all child tasks too

tools/server/server-queue.cpp

@@ -150,31 +150,46 @@ bool server_queue::process_new_tasks(bool is_yielding) {
 
 void server_queue::worker_loop() {
     while (true) {
-        std::function<void()> work;
         {
             std::unique_lock<std::mutex> lock(mutex_tasks);
+            // wait on busy instead of yielding - busy stays set even when the yield already ended
             worker.cv.wait(lock, [&]{
-                return worker.stop || worker.work != nullptr;
+                return worker.stop || worker.busy;
             });
             if (worker.stop) {
                 return;
             }
-            work = std::move(worker.work);
-            worker.work = nullptr;
         }
 
-        // note: do not hold any lock here, work() may post new tasks
-        std::exception_ptr exception;
-        try {
-            work();
-        } catch (...) {
-            exception = std::current_exception();
+        // process tasks while the yield is active
+        while (true) {
+            bool terminated = false;
+            try {
+                // note: do not hold any lock here, the callback may post new tasks
+                terminated = process_new_tasks(true);
+            } catch (...) {
+                std::unique_lock<std::mutex> lock(mutex_tasks);
+                worker.exception = std::current_exception();
+                break;
+            }
+
+            std::unique_lock<std::mutex> lock(mutex_tasks);
+            if (terminated || worker.stop || !worker.yielding) {
+                break;
+            }
+            if (!queue_tasks.empty()) {
+                continue; // a new task arrived in the meantime
+            }
+            condition_tasks.wait(lock, [&]{
+                return worker.stop || !running || !worker.yielding || !queue_tasks.empty();
+            });
         }
 
-        // signal completion to yield_to_queue()
-        std::unique_lock<std::mutex> lock(mutex_tasks);
-        worker.exception = std::move(exception);
-        worker.busy = false;
+        // signal to yield_to_queue() that no more tasks will be processed
+        {
+            std::unique_lock<std::mutex> lock(mutex_tasks);
+            worker.busy = false;
+        }
         condition_tasks.notify_all();
     }
 }
@@ -188,6 +203,7 @@ void server_queue::worker_stop() {
         worker.stop = true;
     }
     worker.cv.notify_one();
+    condition_tasks.notify_all();
     worker.thread.join();
 }
 
@@ -199,29 +215,29 @@ void server_queue::yield_to_queue(std::function<void()> && work) {
     {
         std::unique_lock<std::mutex> lock(mutex_tasks);
         GGML_ASSERT(!worker.busy && "yield_to_queue() cannot be nested");
-        worker.busy = true;
-        worker.work = std::move(work);
+        worker.busy     = true;
+        worker.yielding = true;
     }
     worker.cv.notify_one();
 
-    while (true) {
-        // note: on terminate this is a no-op, but we still wait for the work to finish
-        process_new_tasks(true);
-
-        std::unique_lock<std::mutex> lock(mutex_tasks);
-        // declined tasks are moved to queue_tasks_unhandled, so a non-empty queue always has something new
-        condition_tasks.wait(lock, [&]{
-            return !worker.busy || (running && !queue_tasks.empty());
-        });
-        if (!worker.busy) {
-            break;
-        }
+    // run the work on the current thread, so that all ggml compute stays on the same thread
+    std::exception_ptr exception;
+    try {
+        work();
+    } catch (...) {
+        exception = std::current_exception();
     }
 
-    std::exception_ptr exception;
     {
         std::unique_lock<std::mutex> lock(mutex_tasks);
 
+        // the yield is over, wait for the worker to finish its current task
+        worker.yielding = false;
+        condition_tasks.notify_all();
+        condition_tasks.wait(lock, [&]{
+            return !worker.busy;
+        });
+
         // put the declined tasks back, keeping their order
         while (!queue_tasks_unhandled.empty()) {
             queue_tasks.push_front(std::move(queue_tasks_unhandled.back()));
@@ -231,8 +247,12 @@ void server_queue::yield_to_queue(std::function<void()> && work) {
         // make sure to avoid idle timeout here
         time_last_task = ggml_time_ms();
 
-        // the worker is idle now, take the exception it may have left behind
-        std::swap(exception, worker.exception);
+        // an exception from work() takes precedence over the one from the worker
+        if (!exception) {
+            std::swap(exception, worker.exception);
+        } else {
+            worker.exception = nullptr;
+        }
     }
 
     QUE_DBG("%s", "done yielding to queue\n");
@@ -249,7 +269,9 @@ void server_queue::start_loop(int64_t idle_sleep_ms) {
 
     // spawn the worker thread used by yield_to_queue()
     GGML_ASSERT(!worker.thread.joinable() && "start_loop() is already running");
-    worker.stop = false;
+    worker.stop     = false;
+    worker.busy     = false;
+    worker.yielding = false;
     worker.thread = std::thread([this]() { worker_loop(); });
 
     constexpr auto max_wait_time = std::chrono::seconds(1);

tools/server/server-queue.h

@@ -33,11 +33,11 @@ private:
     // used by yield_to_queue, all fields are guarded by mutex_tasks
     struct worker_t {
         std::thread             thread;
-        std::condition_variable cv;   // the worker sleeps on this until there is work
-        std::function<void()>   work; // pending work, picked up by the thread
-        std::exception_ptr      exception; // exception thrown by work(), if any
-        bool stop = false;
-        bool busy = false;
+        std::condition_variable cv;        // the worker sleeps on this until a yield starts
+        std::exception_ptr      exception; // exception thrown while processing tasks, if any
+        bool stop     = false;
+        bool busy     = false; // set by yield_to_queue(), cleared by the worker once it is done processing tasks
+        bool yielding = false; // work() is still running on the start_loop() thread
     };
     worker_t worker;
 
@@ -93,7 +93,7 @@ public:
      */
     void start_loop(int64_t idle_sleep_ms = -1);
 
-    // run work() on a separate thread, while the current thread calls process_new_tasks
+    // while waiting for work() to finish, run process_new_tasks on the worker thread
     // returns once work() is done (may throw exceptions)
     // must be called from start_loop() thread (ideally inside callback_update_slots)
     // use case: return metrics while encode/decode is running
@@ -116,6 +116,7 @@ public:
     // the second argument tells whether the queue is currently yielding (see yield_to_queue)
     // only then may the callback return false to decline the task, and it must leave it
     // untouched, so that it can be put back in the queue later
+    // note: while yielding, the callback runs on worker thread, not main thread
     void on_new_task(std::function<bool(server_task &&, bool)> callback) {
         callback_new_task = std::move(callback);
     }

tools/ui/src/lib/components/app/server/ServerErrorSplash.svelte

@@ -158,6 +158,8 @@
 					<div class="relative">
 						<Input
 							id="api-key-input"
+							type="password"
+							autocomplete="new-password"
 							placeholder="Enter your API key..."
 							bind:value={apiKeyInput}
 							onkeydown={handleApiKeyKeydown}

tools/ui/src/lib/components/app/settings/SettingsChat/SettingsChatFields.svelte

@@ -81,7 +81,8 @@
 				<div class="relative w-full">
 					<Input
 						id={field.key}
-						type={field.isPositiveInteger ? 'number' : 'text'}
+						type={field.isPrivate ? 'password' : field.isPositiveInteger ? 'number' : 'text'}
+						autocomplete={field.isPrivate ? 'new-password' : undefined}
 						{...field.isPositiveInteger
 							? {
 									min: String(field.min ?? 1),

tools/ui/src/lib/constants/settings-registry.constants.ts

@@ -324,6 +324,7 @@ const SETTINGS_REGISTRY: Record<string, SettingsSectionEntry> = {
 			{
 				defaultValue: '',
 				help: `Set the API Key if you are using <code> ${CLI_FLAGS.API_KEY} </code> option for the server.`,
+				isPrivate: true,
 				key: SETTINGS_KEYS.API_KEY,
 				label: 'API Key',
 				section: SETTINGS_SECTION_SLUGS.GENERAL,
@@ -713,6 +714,7 @@ export const SETTINGS_CHAT_SECTIONS: SettingsSection[] = [
 			help: s.help,
 			isExperimental: s.isExperimental,
 			isPositiveInteger: s.isPositiveInteger,
+			isPrivate: s.isPrivate,
 			key: s.key,
 			label: s.label,
 			max: s.max,

tools/ui/src/lib/services/mcp.service.ts

@@ -18,7 +18,8 @@ import {
 	DEFAULT_CLIENT_VERSION,
 	DEFAULT_IMAGE_MIME_TYPE,
 	DEFAULT_MCP_CONFIG,
-	HEADERS
+	HEADERS,
+	NEWLINE
 } from '$lib/constants';
 import {
 	MCPConnectionPhase,
@@ -70,6 +71,7 @@ interface ToolResultContentItem {
 
 interface ToolCallResult {
 	content?: ToolResultContentItem[];
+	structuredContent?: Record<string, unknown>;
 	isError?: boolean;
 	_meta?: Record<string, unknown>;
 }
@@ -1012,10 +1014,20 @@ export class MCPService {
 
 		if (!Array.isArray(content)) return '';
 
-		return content
+		const formatted = content
 			.map((item) => this.formatSingleContent(item))
 			.filter(Boolean)
-			.join('\n');
+			.join(NEWLINE);
+
+		if (formatted !== '') {
+			return formatted;
+		}
+
+		if (result.structuredContent && typeof result.structuredContent === 'object') {
+			return JSON.stringify(result.structuredContent);
+		}
+
+		return '';
 	}
 
 	private static formatSingleContent(content: ToolResultContentItem): string {

tools/ui/src/lib/types/settings.d.ts

@@ -31,6 +31,7 @@ export interface SettingsEntry {
 	radioOptions?: Array<{ value: string; label: string; key: string; isExperimental?: boolean }>;
 	isExperimental?: boolean;
 	isPositiveInteger?: boolean;
+	isPrivate?: boolean;
 	placeholder?: string;
 	min?: number;
 	max?: number;
@@ -55,6 +56,7 @@ export interface SettingsFieldConfig {
 	type: SettingsFieldType;
 	isExperimental?: boolean;
 	isPositiveInteger?: boolean;
+	isPrivate?: boolean;
 	placeholder?: string;
 	min?: number;
 	max?: number;

tools/ui/tests/unit/mcp-service.test.ts

@@ -2,7 +2,7 @@ import { Client } from '@modelcontextprotocol/sdk/client';
 import { CORS_PROXY } from '$lib/constants';
 import { MCPConnectionPhase, MCPTransportType } from '$lib/enums';
 import { MCPService } from '$lib/services/mcp.service';
-import type { MCPConnectionLog, MCPServerConfig } from '$lib/types';
+import type { MCPConnection, MCPConnectionLog, MCPServerConfig } from '$lib/types';
 import { afterEach, describe, expect, it, vi } from 'vitest';
 
 type DiagnosticFetchFactory = (
@@ -329,4 +329,21 @@ describe('MCPService', () => {
 			)
 		).toHaveLength(0);
 	});
+
+	it('falls back to structuredContent when content array is empty', async () => {
+		const connection = {
+			client: {
+				callTool: vi.fn().mockResolvedValue({
+					content: [],
+					structuredContent: { accounts: [{ id: 1 }], total: 1 }
+				})
+			},
+			requestTimeoutMs: 9000,
+			serverName: 'test-server'
+		} as unknown as MCPConnection;
+		const result = await MCPService.callTool(connection, { arguments: {}, name: 'tool' });
+
+		expect(result.isError).toBe(false);
+		expect(result.content).toBe('{"accounts":[{"id":1}],"total":1}');
+	});
 });

tools/ui/tests/unit/settings-private-fields.test.ts

@@ -0,0 +1,12 @@
+import { SETTINGS_CHAT_SECTIONS, SETTINGS_KEYS } from '$lib/constants';
+import { describe, expect, it } from 'vitest';
+
+describe('checkApiKeyField', () => {
+	it('should have isPrivate set to true', () => {
+		const fields = SETTINGS_CHAT_SECTIONS.flatMap((section) => section.fields);
+		const apiKeyField = fields.find((field) => field?.key === SETTINGS_KEYS.API_KEY);
+
+		expect(apiKeyField).toBeDefined();
+		expect(apiKeyField?.isPrivate).toBe(true);
+	});
+});

vendor/cpp-httplib/CMakeLists.txt

@@ -41,7 +41,7 @@ if (LLAMA_BUILD_BORINGSSL)
     set(FIPS OFF CACHE BOOL "Enable FIPS (BoringSSL)")
 
     set(BORINGSSL_GIT "https://boringssl.googlesource.com/boringssl" CACHE STRING "BoringSSL git repository")
-    set(BORINGSSL_VERSION "0.20260803.0" CACHE STRING "BoringSSL version")
+    set(BORINGSSL_VERSION "0.20260813.0" CACHE STRING "BoringSSL version")
 
     message(STATUS "Fetching BoringSSL version ${BORINGSSL_VERSION}")
 

vendor/cpp-httplib/httplib.cpp

@@ -7146,6 +7146,10 @@ PathParamsMatcher::PathParamsMatcher(const std::string &pattern)
 bool PathParamsMatcher::match(Request &request) const {
   request.matches = std::smatch();
   request.path_params.clear();
+
+  // A pattern without parameters is just a literal path to compare against
+  if (param_names_.empty()) { return request.path == pattern(); }
+
   request.path_params.reserve(param_names_.size());
 
   // One past the position at which the path matched the pattern last time
@@ -7188,6 +7192,11 @@ bool PathParamsMatcher::match(Request &request) const {
 
 bool RegexMatcher::match(Request &request) const {
   request.path_params.clear();
+  // See CPPHTTPLIB_REGEX_ROUTE_PATH_MAX_LENGTH: an overlong path is treated as
+  // a non-match rather than risking a stack overflow in std::regex_match.
+  if (request.path.length() > CPPHTTPLIB_REGEX_ROUTE_PATH_MAX_LENGTH) {
+    return false;
+  }
   return std::regex_match(request.path, request.matches, regex_);
 }
 
@@ -7613,11 +7622,21 @@ Server::~Server() = default;
 
 std::unique_ptr<detail::MatcherBase>
 Server::make_matcher(const std::string &pattern) {
+  // Path params take precedence, so "/users/:id/(.*)" keeps being matched as
+  // a path params pattern
   if (pattern.find("/:") != std::string::npos) {
     return detail::make_unique<detail::PathParamsMatcher>(pattern);
-  } else {
-    return detail::make_unique<detail::RegexMatcher>(pattern);
   }
+
+  // A pattern with no regex metacharacter only has to be compared literally,
+  // which is what PathParamsMatcher already does when it captures no
+  // parameter, so std::regex is only worth building for the patterns that
+  // actually need it
+  if (pattern.find_first_of(".^$|()[]{}*+?\\") == std::string::npos) {
+    return detail::make_unique<detail::PathParamsMatcher>(pattern);
+  }
+
+  return detail::make_unique<detail::RegexMatcher>(pattern);
 }
 
 Server &Server::Get(const std::string &pattern, Handler handler) {
@@ -8259,15 +8278,12 @@ bool Server::read_content_core(
         }
       }
       if (has_data) {
-        auto result =
-            detail::read_content_without_length(strm, payload_max_length_, out);
-        if (result == detail::ReadContentResult::PayloadTooLarge) {
-          res.status = StatusCode::PayloadTooLarge_413;
-          return false;
-        } else if (result != detail::ReadContentResult::Success) {
-          return false;
-        }
-        return true;
+        // Route through the same decompressing reader used by the
+        // length-framed and chunked paths below, so payload_max_length_ is
+        // enforced on the decompressed size here too instead of only on the
+        // compressed wire bytes.
+        return detail::read_content(strm, req, payload_max_length_, res.status,
+                                    nullptr, out, true);
       }
     }
     return true;

vendor/cpp-httplib/httplib.h

@@ -8,8 +8,8 @@
 #ifndef CPPHTTPLIB_HTTPLIB_H
 #define CPPHTTPLIB_HTTPLIB_H
 
-#define CPPHTTPLIB_VERSION "0.53.0"
-#define CPPHTTPLIB_VERSION_NUM "0x003500"
+#define CPPHTTPLIB_VERSION "0.53.1"
+#define CPPHTTPLIB_VERSION_NUM "0x003501"
 
 #ifdef _WIN32
 #if defined(_WIN32_WINNT) && _WIN32_WINNT < 0x0A00
@@ -138,6 +138,18 @@
 #define CPPHTTPLIB_RANGE_MAX_COUNT 1024
 #endif
 
+// std::regex_match's backtracking implementation (most acutely on libstdc++)
+// recurses roughly once per matched character for quantified patterns such
+// as "(.*)", so a long enough path can exhaust the calling thread's stack; on
+// a default ~8MB thread stack that has been observed to take on the order of
+// a couple thousand characters for a simple pattern. 256 leaves a wide safety
+// margin below that (well under the 8192-byte request URI limit) while still
+// fitting any realistic route segment; raise it if a route legitimately needs
+// longer paths. Regex routes are never applied to paths longer than this.
+#ifndef CPPHTTPLIB_REGEX_ROUTE_PATH_MAX_LENGTH
+#define CPPHTTPLIB_REGEX_ROUTE_PATH_MAX_LENGTH 256
+#endif
+
 #ifndef CPPHTTPLIB_TCP_NODELAY
 #define CPPHTTPLIB_TCP_NODELAY false
 #endif
@@ -839,6 +851,15 @@ inline bool parse_url(const std::string &url, UrlComponents &uc) {
       }
 
       pos = close + 1;
+
+      // The IPv6 literal is the whole host, so ']' must be followed by a port,
+      // path, query or fragment delimiter (or the end of input). Otherwise the
+      // trailing bytes would be folded into the path while the connection
+      // still targets the bracketed address.
+      if (pos < url.size()) {
+        auto c = url[pos];
+        if (c != ':' && c != '/' && c != '?' && c != '#') { return false; }
+      }
     } else {
       auto end = url.find_first_of(":/?#", pos);
       if (end == std::string::npos) { end = url.size(); }