server: improve user message detection and create checkpoints at every user message (#24176)

* server : improve message span logic

* cont : cast size_t to int32_t in comparisons

* server : create checkpoints before every user msg

* chat : remove \n in gemma4 delimiters

* chat : merge msg delimiter structs into one

* cont : reword comment

* cont : initialize tokens in delimiter

* cont : add server_tokens::get_raw_tokens() for mtmd

* cont : move message finding to server_tokens and skip mtmd tokens

* cont : update cohere2moe parser

* cont : increase min-step to 8192 and always produce a chkpt for last user message

common/chat.cpp

@@ -90,41 +90,93 @@ std::string common_chat_msg::render_content(const std::string & delimiter) const
     return text;
 }
 
-std::vector<common_chat_msg_span> common_chat_split_by_role(const std::string & prompt, const std::vector<common_chat_msg_delimiter> & delims) {
-    if (delims.empty() || prompt.empty()) {
-        return {};
+common_chat_role common_chat_role_from_string(const std::string & role) {
+    if (role == "system")    { return COMMON_CHAT_ROLE_SYSTEM;    }
+    if (role == "assistant") { return COMMON_CHAT_ROLE_ASSISTANT; }
+    if (role == "user")      { return COMMON_CHAT_ROLE_USER;      }
+    if (role == "tool")      { return COMMON_CHAT_ROLE_TOOL;      }
+    return COMMON_CHAT_ROLE_UNKNOWN;
+}
+
+const char * common_chat_role_to_string(common_chat_role role) {
+    switch (role) {
+        case COMMON_CHAT_ROLE_SYSTEM:    return "system";
+        case COMMON_CHAT_ROLE_ASSISTANT: return "assistant";
+        case COMMON_CHAT_ROLE_USER:      return "user";
+        case COMMON_CHAT_ROLE_TOOL:      return "tool";
+        case COMMON_CHAT_ROLE_UNKNOWN:   return "";
+    }
+    return "";
+}
+
+json common_chat_msg_delimiters::to_json() const {
+    json result = json::array();
+    for (const auto & d : delimiters) {
+        result.push_back({
+            { "role",      common_chat_role_to_string(d.role) },
+            { "delimiter", d.delimiter                        },
+        });
+    }
+    return result;
+}
+
+common_chat_msg_delimiters common_chat_msg_delimiters_parse(const json & delimiters) {
+    common_chat_msg_delimiters result;
+
+    if (!delimiters.is_array()) {
+        return result;
     }
 
-    auto parser = build_peg_parser([&](common_peg_parser_builder & p) {
-        std::vector<std::string>       all_delims;
-        std::vector<common_peg_parser> tagged_messages;
-
-        all_delims.reserve(delims.size());
-        tagged_messages.reserve(delims.size());
-        for (const auto & d : delims) {
-            all_delims.push_back(d.delimiter);
+    result.delimiters.reserve(delimiters.size());
+    for (const auto & d : delimiters) {
+        if (!d.is_object()) {
+            continue;
         }
-
-        auto any_delim = p.until_one_of(all_delims);
-        for (const auto & d : delims) {
-            tagged_messages.push_back(p.tag(d.role, p.literal(d.delimiter) + any_delim));
-        }
-
-        return any_delim + p.zero_or_more(p.choice(tagged_messages)) + p.end();
-    });
-
-    common_peg_parse_context ctx(prompt);
-    const auto result = parser.parse(ctx);
-    if (!result.success()) {
-        return {};
+        result.delimiters.push_back({
+            common_chat_role_from_string(d.value("role", std::string())),
+            d.value("delimiter", std::string()),
+        });
     }
 
-    std::vector<common_chat_msg_span> spans;
-    ctx.ast.visit(result, [&](const common_peg_ast_node & node) {
-        if (!node.tag.empty()) {
-            spans.push_back({ node.tag, node.start, node.end - node.start });
+    return result;
+}
+
+void common_chat_msg_delimiters::tokenize(const llama_vocab * vocab) {
+    for (auto & d : delimiters) {
+        d.tokens = common_tokenize(vocab, d.delimiter, false, true);
+    }
+}
+
+common_chat_msg_spans common_chat_msg_delimiters::split(const llama_tokens & tokens, const std::map<size_t, size_t> & skips) const {
+    std::vector<std::pair<common_chat_role, size_t>> matches;
+
+    auto skip = skips.begin();
+    for (size_t i = 0; i < tokens.size();) {
+        if (skip != skips.end() && i == skip->first) {
+            i += skip->second;
+            ++skip;
+            continue;
         }
-    });
+        for (const auto & d : delimiters) {
+            if (i + d.tokens.size() > tokens.size()) {
+                continue;
+            }
+            if (std::equal(d.tokens.begin(), d.tokens.end(), tokens.begin() + i)) {
+                matches.emplace_back(d.role, i);
+                break;
+            }
+        }
+        i++;
+    }
+
+    matches.emplace_back(COMMON_CHAT_ROLE_UNKNOWN, tokens.size());
+
+    common_chat_msg_spans spans;
+    for (size_t i = 0; i + 1 < matches.size(); i++) {
+        const auto & curr = matches[i];
+        const auto & next = matches[i + 1];
+        spans.add(curr.first, curr.second, next.second - curr.second);
+    }
 
     return spans;
 }
@@ -1081,13 +1133,13 @@ static common_chat_params common_chat_params_init_gpt_oss(const common_chat_temp
 
     data.prompt            = prompt;
     data.generation_prompt = common_chat_template_generation_prompt_impl(tmpl, inputs, /* messages_override= */ adjusted_messages);
-    data.message_spans = common_chat_split_by_role(prompt, {
-        { "assistant", "<|start|>assistant" },
-        { "user",      "<|start|>user"      },
-        { "system",    "<|start|>developer" },
-        { "system",    "<|start|>system"    },
-        { "tool",      "<|start|>functions" },
-    });
+    data.message_delimiters = {
+        { COMMON_CHAT_ROLE_ASSISTANT, "<|start|>assistant" },
+        { COMMON_CHAT_ROLE_USER,      "<|start|>user"      },
+        { COMMON_CHAT_ROLE_SYSTEM,    "<|start|>developer" },
+        { COMMON_CHAT_ROLE_SYSTEM,    "<|start|>system"    },
+        { COMMON_CHAT_ROLE_TOOL,      "<|start|>functions" },
+    };
 
     data.format            = COMMON_CHAT_FORMAT_PEG_NATIVE;
     data.supports_thinking = true;
@@ -1228,10 +1280,10 @@ static common_chat_params common_chat_params_init_gemma4(const common_chat_templ
         data.prompt += data.generation_prompt;
     }
 
-    data.message_spans = common_chat_split_by_role(data.prompt, {
-        { "user",      "<|turn>user\n"  },
-        { "assistant", "<|turn>model\n" },
-    });
+    data.message_delimiters = {
+        { COMMON_CHAT_ROLE_USER,      "<|turn>user"  },
+        { COMMON_CHAT_ROLE_ASSISTANT, "<|turn>model" },
+    };
 
     data.format            = COMMON_CHAT_FORMAT_PEG_GEMMA4;
     data.supports_thinking  = true;
@@ -2030,15 +2082,15 @@ static common_chat_params common_chat_params_init_cohere2moe(const common_chat_t
         RESULT_START, RESULT_END,
     };
 
-    // Split the rendered prompt into per-role message spans. Tool results are rendered with the
+    // Declare per-role message delimiters. Tool results are rendered with the
     // system token followed by <|START_TOOL_RESULT|>, so the "tool" delimiter must be listed before
     // the plain "system" one (it is a strict superset, and the role split tries delimiters in order).
-    data.message_spans = common_chat_split_by_role(data.prompt, {
-        { "assistant", GEN_PREFIX },
-        { "user",      TURN_START + USER },
-        { "tool",      TURN_START + SYSTEM + RESULT_START },
-        { "system",    TURN_START + SYSTEM },
-    });
+    data.message_delimiters = {
+        { COMMON_CHAT_ROLE_ASSISTANT, GEN_PREFIX },
+        { COMMON_CHAT_ROLE_USER,      TURN_START + USER },
+        { COMMON_CHAT_ROLE_TOOL,      TURN_START + SYSTEM + RESULT_START },
+        { COMMON_CHAT_ROLE_SYSTEM,    TURN_START + SYSTEM },
+    };
 
     auto has_tools         = inputs.tools.is_array() && !inputs.tools.empty();
     auto extract_reasoning = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE;
@@ -2526,17 +2578,15 @@ static common_chat_params common_chat_templates_apply_jinja(const struct common_
         autoparser.analyze_template(tmpl);
         auto auto_params = autoparser::peg_generator::generate_parser(tmpl, params, autoparser);
 
-        std::vector<common_chat_msg_delimiter> delimiters;
+        common_chat_msg_delimiters delimiters;
         if (!autoparser.assistant_start.empty()) {
-            delimiters.push_back({ "assistant", autoparser.assistant_start });
+            delimiters.add(COMMON_CHAT_ROLE_ASSISTANT, autoparser.assistant_start);
         }
         if (!autoparser.user_start.empty()) {
-            delimiters.push_back({ "user", autoparser.user_start });
+            delimiters.add(COMMON_CHAT_ROLE_USER, autoparser.user_start);
         }
 
-        if (!delimiters.empty()) {
-            auto_params.message_spans = common_chat_split_by_role(auto_params.prompt, delimiters);
-        }
+        auto_params.message_delimiters = std::move(delimiters);
 
         auto_params.supports_thinking = autoparser.reasoning.mode != autoparser::reasoning_mode::NONE;
         if (auto_params.supports_thinking) {

common/chat.h

@@ -143,15 +143,75 @@ struct common_chat_msg_diff {
     }
 };
 
+enum common_chat_role {
+    COMMON_CHAT_ROLE_UNKNOWN,
+    COMMON_CHAT_ROLE_SYSTEM,
+    COMMON_CHAT_ROLE_ASSISTANT,
+    COMMON_CHAT_ROLE_USER,
+    COMMON_CHAT_ROLE_TOOL
+};
+
+common_chat_role common_chat_role_from_string(const std::string & role);
+const char *     common_chat_role_to_string(common_chat_role role);
+
 struct common_chat_msg_span {
-    std::string role;
+    common_chat_role role = COMMON_CHAT_ROLE_UNKNOWN;
     std::size_t pos = 0;
     std::size_t len = 0;
+
+    bool valid() const {
+        return role != COMMON_CHAT_ROLE_UNKNOWN;
+    }
+};
+
+struct common_chat_msg_spans {
+    std::vector<common_chat_msg_span> spans;
+
+    void add(common_chat_role role, size_t pos, size_t len) {
+        spans.push_back({ role, pos, len });
+    }
+
+    bool is_user_start(int32_t pos) const {
+        for (auto it = spans.begin(); it != spans.end(); ++it) {
+            if (it->role == COMMON_CHAT_ROLE_USER && pos == (int32_t) it->pos) {
+                return true;
+            }
+        }
+        return false;
+    }
+
+    int32_t last_user_message_pos() const {
+        for (auto it = spans.rbegin(); it != spans.rend(); ++it) {
+            if (it->role == COMMON_CHAT_ROLE_USER) {
+                return (int32_t) it->pos;
+            }
+        }
+        return -1;
+    }
 };
 
 struct common_chat_msg_delimiter {
-    std::string role;
-    std::string delimiter;
+    common_chat_role role = COMMON_CHAT_ROLE_UNKNOWN;
+    std::string      delimiter;
+    llama_tokens     tokens = {};
+};
+
+struct common_chat_msg_delimiters {
+    std::vector<common_chat_msg_delimiter> delimiters;
+
+    common_chat_msg_delimiters() = default;
+    common_chat_msg_delimiters(std::initializer_list<common_chat_msg_delimiter> delims) : delimiters(delims) {}
+
+    void add(common_chat_role role, const std::string & delimiter) {
+        delimiters.push_back({ role, delimiter });
+    }
+
+    void tokenize(const llama_vocab * vocab);
+
+    // split tokens into message spans. skips maps a start index to a length of a region to jump over without matching
+    common_chat_msg_spans split(const llama_tokens & tokens, const std::map<size_t, size_t> & skips = {}) const;
+
+    nlohmann::ordered_json to_json() const;
 };
 
 struct common_chat_tool {
@@ -219,7 +279,7 @@ struct common_chat_params {
     std::vector<std::string>            preserved_tokens;
     std::vector<std::string>            additional_stops;
     std::string                         parser;
-    std::vector<common_chat_msg_span>   message_spans;
+    common_chat_msg_delimiters          message_delimiters;
 };
 
 // per-message parsing syntax
@@ -325,5 +385,4 @@ struct common_chat_prompt_preset {
 
 common_chat_prompt_preset common_chat_get_asr_prompt(const common_chat_templates * chat_templates);
 
-std::vector<common_chat_msg_span> common_chat_split_by_role(const std::string & prompt, const std::vector<common_chat_msg_delimiter> & delims);
-
+common_chat_msg_delimiters common_chat_msg_delimiters_parse(const nlohmann::ordered_json & delimiters);

common/common.h

@@ -609,7 +609,7 @@ struct common_params {
     bool    cache_prompt        = true;  // whether to enable prompt caching
     bool    cache_idle_slots    = true;  // save and clear idle slots upon starting a new task
     int32_t n_ctx_checkpoints   = 32;    // max number of context checkpoints per slot
-    int32_t checkpoint_min_step = 256;   // minimum spacing between context checkpoints
+    int32_t checkpoint_min_step = 8192;  // minimum spacing between context checkpoints
     int32_t cache_ram_mib       = 8192;  // -1 = no limit, 0 - disable, 1 = 1 MiB, etc.
 
     std::string hostname      = "127.0.0.1";

ggml/src/ggml-opencl/kernels/gemv_noshuffle_q8_0_f32.cl

@@ -174,7 +174,7 @@ __kernel void kernel_gemv_noshuffle_q8_0_f32(
         regA.s6 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 6)).x;
         regA.s7 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 7)).x;
 
-        dequantizeBlockAccum_ns_sgbroadcast_1(totalSum, regA, regS, regB);
+        dequantizeBlockAccum_ns_sgbroadcast_1(totalSum, regA, convert_float(regS), regB);
     }
 
     // reduction in local memory, assumes #wave=4

tests/test-chat.cpp

@@ -1562,37 +1562,112 @@ static void test_msgs_oaicompat_json_conversion() {
     }
 }
 
-static void test_split_by_role() {
+static void test_msg_token_delimiters_split() {
     LOG_DBG("%s\n", __func__);
 
+    // Delimiters that share a leading token, distinguished by the second token,
+    // to exercise the per-position token matching.
+    const common_chat_msg_delimiters delims = {
+        { { COMMON_CHAT_ROLE_USER,      "", { 10, 11 } },
+          { COMMON_CHAT_ROLE_ASSISTANT, "", { 10, 12 } } }
+    };
+
     // Empty inputs
-    assert_equals<size_t>(0, common_chat_split_by_role("", {}).size());
-    assert_equals<size_t>(0, common_chat_split_by_role("hello", {}).size());
-    assert_equals<size_t>(0, common_chat_split_by_role("", { { "user", "<|user|>" } }).size());
+    assert_equals<size_t>(0, common_chat_msg_delimiters{}.split({}).spans.size());
+    assert_equals<size_t>(0, common_chat_msg_delimiters{}.split({ 10, 11 }).spans.size());
+    assert_equals<size_t>(0, delims.split({}).spans.size());
 
-    // Multi-role conversation, no leading/trailing content
+    // No delimiters match -> no spans
+    assert_equals<size_t>(0, delims.split({ 100, 101, 102 }).spans.size());
+
+    // Multi-role conversation: <user>Hi<assistant>Hello<user>Bye
     {
-        const std::string prompt = "<|user|>Hi<|assistant|>Hello<|user|>Bye";
-        const auto splits = common_chat_split_by_role(prompt, {
-            { "user",      "<|user|>"      },
-            { "assistant", "<|assistant|>" },
-        });
-        assert_equals<size_t>(3, splits.size());
+        const llama_tokens tokens = {
+            10, 11,            // <user>
+            100, 101,          // Hi
+            10, 12,            // <assistant>
+            200, 201, 202,     // Hello
+            10, 11,            // <user>
+            300, 301,          // Bye
+        };
 
-        assert_equals<std::string>("user", splits[0].role);
-        assert_equals<size_t>(0, splits[0].pos);
-        assert_equals<size_t>(10, splits[0].len);
-        assert_equals<std::string>("<|user|>Hi", prompt.substr(splits[0].pos, splits[0].len));
+        const auto result = delims.split(tokens);
+        const auto & spans = result.spans;
+        assert_equals<size_t>(3, spans.size());
 
-        assert_equals<std::string>("assistant", splits[1].role);
-        assert_equals<size_t>(10, splits[1].pos);
-        assert_equals<size_t>(18, splits[1].len);
-        assert_equals<std::string>("<|assistant|>Hello", prompt.substr(splits[1].pos, splits[1].len));
+        assert_equals(COMMON_CHAT_ROLE_USER, spans[0].role);
+        assert_equals<size_t>(0, spans[0].pos);
+        assert_equals<size_t>(4, spans[0].len);
 
-        assert_equals<std::string>("user", splits[2].role);
-        assert_equals<size_t>(28, splits[2].pos);
-        assert_equals<size_t>(11, splits[2].len);
-        assert_equals<std::string>("<|user|>Bye", prompt.substr(splits[2].pos, splits[2].len));
+        assert_equals(COMMON_CHAT_ROLE_ASSISTANT, spans[1].role);
+        assert_equals<size_t>(4, spans[1].pos);
+        assert_equals<size_t>(5, spans[1].len);
+
+        assert_equals(COMMON_CHAT_ROLE_USER, spans[2].role);
+        assert_equals<size_t>(9, spans[2].pos);
+        assert_equals<size_t>(4, spans[2].len);
+
+        // is_user_start() is true at the token position where a user span begins
+        assert_equals(true,  result.is_user_start(0));
+        assert_equals(false, result.is_user_start(4));  // assistant span
+        assert_equals(true,  result.is_user_start(9));
+    }
+
+    // Content before the first delimiter is not captured as a span
+    {
+        const llama_tokens tokens = {
+            500, 501,    // leading content (dropped)
+            10, 11,      // <user>
+            100,         // Hi
+        };
+
+        const auto spans = delims.split(tokens).spans;
+        assert_equals<size_t>(1, spans.size());
+        assert_equals(COMMON_CHAT_ROLE_USER, spans[0].role);
+        assert_equals<size_t>(2, spans[0].pos);
+        assert_equals<size_t>(3, spans[0].len);
+    }
+
+    // Skipped regions (media chunks) are jumped over but still count as span content
+    {
+        const llama_tokens tokens = {
+            10, 11,             // <user>
+            LLAMA_TOKEN_NULL,   // media chunk (3 tokens)
+            LLAMA_TOKEN_NULL,
+            LLAMA_TOKEN_NULL,
+            100,                // Hi
+            10, 12,             // <assistant>
+        };
+
+        const std::map<size_t, size_t> skips = { { 2, 3 } };
+
+        const auto spans = delims.split(tokens, skips).spans;
+        assert_equals<size_t>(2, spans.size());
+
+        assert_equals(COMMON_CHAT_ROLE_USER, spans[0].role);
+        assert_equals<size_t>(0, spans[0].pos);
+        assert_equals<size_t>(6, spans[0].len);
+
+        assert_equals(COMMON_CHAT_ROLE_ASSISTANT, spans[1].role);
+        assert_equals<size_t>(6, spans[1].pos);
+        assert_equals<size_t>(2, spans[1].len);
+    }
+
+    // A delimiter sequence inside a skipped region is not matched
+    {
+        const llama_tokens tokens = {
+            10, 11,      // <user>
+            10, 12,      // skipped region that happens to contain delimiter tokens
+            100,         // Hi
+        };
+
+        const std::map<size_t, size_t> skips = { { 2, 2 } };
+
+        const auto spans = delims.split(tokens, skips).spans;
+        assert_equals<size_t>(1, spans.size());
+        assert_equals(COMMON_CHAT_ROLE_USER, spans[0].role);
+        assert_equals<size_t>(0, spans[0].pos);
+        assert_equals<size_t>(5, spans[0].len);
     }
 }
 
@@ -5857,7 +5932,7 @@ int main(int argc, char ** argv) {
     {
         test_msg_diffs_compute();
         test_msgs_oaicompat_json_conversion();
-        test_split_by_role();
+        test_msg_token_delimiters_split();
         test_tools_oaicompat_json_conversion();
         test_convert_responses_to_chatcmpl();
         test_developer_role_to_system_workaround();

tools/server/server-common.cpp

@@ -518,6 +518,14 @@ size_t server_tokens::get_common_prefix(const server_tokens & b) const {
     return max_idx; // all tokens are equal
 }
 
+common_chat_msg_spans server_tokens::find_message_spans(const common_chat_msg_delimiters & delims) const {
+    std::map<size_t, size_t> skips;
+    for (const auto & it : map_idx_to_media) {
+        skips[it.first] = mtmd_input_chunk_get_n_tokens(it.second.get());
+    }
+    return delims.split(tokens, skips);
+}
+
 bool server_tokens::validate(const struct llama_context * ctx) const {
     const llama_model * model = llama_get_model(ctx);
     const llama_vocab * vocab = llama_model_get_vocab(model);
@@ -1104,15 +1112,7 @@ json oaicompat_chat_params_parse(
         llama_params["chat_parser"] = chat_params.parser;
     }
 
-    llama_params["message_spans"] = json::array();
-
-    for (const auto & span : chat_params.message_spans) {
-        llama_params["message_spans"].push_back({
-            { "role", span.role },
-            { "pos",  span.pos  },
-            { "len",  span.len  },
-        });
-    }
+    llama_params["message_delimiters"] = chat_params.message_delimiters.to_json();
 
     // Reasoning budget: pass parameters through to sampling layer
     {

tools/server/server-common.h

@@ -218,6 +218,9 @@ public:
 
     size_t get_common_prefix(const server_tokens & b) const;
 
+    // split the tokens into message spans, skipping over media chunks
+    common_chat_msg_spans find_message_spans(const common_chat_msg_delimiters & delims) const;
+
     // make sure all text tokens are within the vocab range
     bool validate(const struct llama_context * ctx) const;
 

tools/server/server-context.cpp

@@ -3436,8 +3436,8 @@ private:
                         has_mtmd = true;
                     }
 
-                    const int32_t n_before_user = slot.task->params.n_before_user;
-                    const bool n_before_user_known = n_before_user > 0;
+                    const auto & spans = slot.task->params.message_spans;
+                    const auto last_user_pos = spans.last_user_message_pos();
 
                     // add prompt tokens for processing in the current batch
                     while (slot.prompt.n_tokens() < slot.task->n_tokens() && batch.size() < n_batch) {
@@ -3466,10 +3466,8 @@ private:
 
                         slot.n_prompt_tokens_processed++;
 
-                        // stop the prompt batch exactly before the latest user input, so a checkpoint
-                        // can be created after the previous messages
-                        if (n_before_user_known &&
-                            slot.prompt.n_tokens() == n_before_user) {
+                        // stop the prompt batch exactly before a user message
+                        if (spans.is_user_start(slot.prompt.n_tokens())) {
                             break;
                         }
 
@@ -3498,8 +3496,13 @@ private:
                     // the number of tokens added to the batch for the current slot
                     const auto n_tokens_cur = batch.size() - n_tokens_prev;
 
+                    const auto n_tokens_start = slot.prompt.n_tokens() - n_tokens_cur;
+
                     const bool near_prompt_end = slot.task->n_tokens() < slot.prompt.n_tokens() + n_ubatch;
 
+                    const bool is_user_start = spans.is_user_start(n_tokens_start);
+                    const bool is_last_user_message = n_tokens_start == last_user_pos;
+
                     // entire prompt has been processed
                     if (slot.prompt.n_tokens() == slot.task->n_tokens()) {
                         slot.state = SLOT_STATE_DONE_PROMPT;
@@ -3514,8 +3517,9 @@ private:
 
                         slot.init_sampler();
                     } else {
-                        // skip ordinary mid-prompt checkpoints
-                        if (!n_before_user_known && !near_prompt_end) {
+                        // skip ordinary mid-prompt checkpoints, unless the batch starts a user
+                        // message or we are near the end of the prompt
+                        if (!is_user_start && !near_prompt_end) {
                             do_checkpoint = false;
                         }
                     }
@@ -3523,29 +3527,6 @@ private:
                     const auto pos_min = llama_memory_seq_pos_min(llama_get_memory(ctx_tgt), slot.id);
                     const auto pos_max = llama_memory_seq_pos_max(llama_get_memory(ctx_tgt), slot.id);
 
-                    // checkpoints are created before the current batch is decoded, so
-                    // their token position is the batch start rather than the prompt end
-                    const int32_t n_tokens_start = slot.prompt.n_tokens() - n_tokens_cur;
-
-                    {
-                        const bool is_on_user =
-                            n_before_user_known &&
-                            n_tokens_start == n_before_user;
-
-                        const bool is_after_user =
-                            n_before_user_known &&
-                            n_tokens_start > n_before_user;
-
-                        const bool is_allowed =
-                            !n_before_user_known ||
-                            is_on_user ||
-                            (is_after_user && near_prompt_end);
-
-                        if (do_checkpoint && !is_allowed) {
-                            do_checkpoint = false;
-                        }
-                    }
-
                     // nothing to checkpoint yet
                     // TODO: is this check needed?
                     if (do_checkpoint && pos_min < 0) {
@@ -3555,8 +3536,8 @@ private:
                     // do not checkpoint after mtmd chunks
                     do_checkpoint = do_checkpoint && !has_mtmd;
 
-                    // no need to create checkpoints that are too close together
-                    do_checkpoint = do_checkpoint && (slot.prompt.checkpoints.empty() || n_tokens_start > slot.prompt.checkpoints.back().n_tokens + params_base.checkpoint_min_step);
+                    // no need to create checkpoints that are too close together, unless it's the last user message
+                    do_checkpoint = do_checkpoint && (slot.prompt.checkpoints.empty() || is_last_user_message || n_tokens_start > slot.prompt.checkpoints.back().n_tokens + params_base.checkpoint_min_step);
                     SLT_DBG(slot, "main/do_checkpoint = %s, pos_min = %d, pos_max = %d\n", do_checkpoint ? "yes" : "no", pos_min, pos_max);
 
                     // note: we create the checkpoint before calling llama_decode(), so the current batch is not
@@ -4055,54 +4036,6 @@ void server_context::set_state_callback(server_state_callback_t callback) {
     });
 }
 
-// compute the number of tokens before the last user message in the prompt
-static int32_t prompt_get_n_before_user(
-        const json & message_spans,
-        const std::string & prompt,
-        const std::vector<raw_buffer> & files,
-        const llama_vocab * vocab,
-        mtmd_context * mctx) {
-    int32_t result = -1;
-    int32_t byte_pos = -1;
-
-    for (const auto & span : message_spans) {
-        const std::string role = json_value(span, "role", std::string());
-
-        if (role == "user") {
-            byte_pos = json_value(span, "pos", -1);
-        }
-    }
-
-    if (byte_pos >= 0) {
-        GGML_ASSERT((size_t) byte_pos <= prompt.size());
-
-        const std::string prefix = prompt.substr(0, (size_t) byte_pos);
-
-        const std::string marker = get_media_marker();
-        size_t n_prefix_media = 0;
-        for (size_t pos = 0; (pos = prefix.find(marker, pos)) != std::string::npos; pos += marker.size()) {
-            n_prefix_media++;
-        }
-
-        GGML_ASSERT(n_prefix_media <= files.size());
-
-        if (mctx != nullptr && n_prefix_media > 0) {
-            // TODO: this makes a copy - avoid it
-            std::vector<raw_buffer> prefix_files(files.begin(), files.begin() + n_prefix_media);
-
-            result = (int32_t) process_mtmd_prompt(mctx, prefix, prefix_files).size();
-        } else {
-            result = (int32_t) tokenize_input_prompts(vocab, nullptr, prefix, true, true)[0].size();
-        }
-
-        SRV_TRC("message_spans: last user message: byte_pos=%d, media=%zu, n_before_user=%d\n",
-                byte_pos, n_prefix_media, result);
-    }
-
-    return result;
-}
-
-
 //
 // server_routes
 //
@@ -4150,6 +4083,10 @@ std::unique_ptr<server_res_generator> server_routes::handle_completions_impl(
 
         // tasks.reserve(inputs.size()); // TODO: this is inaccurate due to child tasks
 
+        // message delimiters for checkpointing
+        auto delimiters = common_chat_msg_delimiters_parse(json_value(data, "message_delimiters", json::array()));
+        delimiters.tokenize(ctx_server.vocab);
+
         for (size_t i = 0; i < inputs.size(); i++) {
             server_task task = server_task(type);
 
@@ -4163,16 +4100,7 @@ std::unique_ptr<server_res_generator> server_routes::handle_completions_impl(
                     meta->logit_bias_eog,
                     data);
 
-            const auto message_spans = json_value(data, "message_spans", json::array());
-            if (prompt.is_string() && message_spans.is_array()) {
-                task.params.n_before_user =
-                    prompt_get_n_before_user(
-                        message_spans,
-                        prompt.get<std::string>(),
-                        files,
-                        ctx_server.vocab,
-                        ctx_server.mctx);
-            }
+            task.params.message_spans = task.tokens.find_message_spans(delimiters);
 
             task.id_slot = json_value(data, "id_slot", -1);
 

tools/server/server-task.cpp

@@ -591,10 +591,11 @@ json server_task_result_cmpl_final::to_json_oaicompat_resp() {
 
     for (const common_chat_tool_call & tool_call : oaicompat_msg.tool_calls) {
         output.push_back(json {
+            {"id",        "fc_" + tool_call.id},
             {"type",      "function_call"},
             {"status",    "completed"},
             {"arguments", tool_call.arguments},
-            {"call_id",   "fc_" + tool_call.id},
+            {"call_id",   "call_" + tool_call.id},
             {"name",      tool_call.name},
         });
     }
@@ -690,10 +691,11 @@ json server_task_result_cmpl_final::to_json_oaicompat_resp_stream() {
 
     for (const common_chat_tool_call & tool_call : oaicompat_msg.tool_calls) {
         const json output_item = {
+            {"id",        "fc_" + tool_call.id},
             {"type",      "function_call"},
             {"status",    "completed"},
             {"arguments", tool_call.arguments},
-            {"call_id",   "fc_" + tool_call.id},
+            {"call_id",   "call_" + tool_call.id},
             {"name",      tool_call.name}
         };
         server_sent_events.push_back(json {
@@ -1277,8 +1279,9 @@ json server_task_result_cmpl_partial::to_json_oaicompat_resp() {
                 {"data", json {
                     {"type",  "response.output_item.added"},
                     {"item", json {
+                        {"id",        "fc_" + diff.tool_call_delta.id},
                         {"arguments", ""},
-                        {"call_id",   "fc_" + diff.tool_call_delta.id},
+                        {"call_id",   "call_" + diff.tool_call_delta.id},
                         {"name",      diff.tool_call_delta.name},
                         {"type",      "function_call"},
                         {"status",    "in_progress"},

tools/server/server-task.h

@@ -62,9 +62,6 @@ struct task_params {
 
     int32_t n_cache_reuse = 0; // min chunk size to attempt reusing from the cache via KV shifting (0 = disabled)
 
-    // number of prompt tokens before the latest user message
-    int32_t n_before_user = -1;
-
     int64_t t_max_prompt_ms  = -1; // TODO: implement
     int64_t t_max_predict_ms = -1; // if positive, limit the generation phase to this time limit
 
@@ -92,6 +89,9 @@ struct task_params {
     // per-request parameters for chat parsing
     common_chat_parser_params chat_parser_params;
 
+    // message spans for checkpointing
+    common_chat_msg_spans message_spans;
+
     // Embeddings
     int32_t embd_normalize = 2; // (-1=none, 0=max absolute int16, 1=taxicab, 2=Euclidean/L2, >2=p-norm)
 

tools/ui/src/lib/stores/models.svelte.ts

@@ -545,7 +545,8 @@ class ModelsStore {
 	 * 1. Model from active conversation's last assistant response (if loaded)
 	 * 2. Model from active conversation's last assistant response (if not loaded)
 	 * 3. First loaded model (not from active conversation)
-	 * 4. First available model
+	 * 4. A favorite model
+	 * 5. First available model
 	 */
 	async ensureFirstModelSelected(): Promise<void> {
 		if (this.selectedModelName) return;
@@ -574,6 +575,13 @@ class ModelsStore {
 			return;
 		}
 
+		// Try loading a favorite model
+		const favorite = this.favoriteModelIds.values().next()?.value
+		if (favorite) {
+			await this.selectModelById(favorite);
+			return;
+		}
+
 		// Fall back to the first available model
 		await this.selectModelById(availableModels[0].id);
 	}