Merge branch 'upstream' into concedo_experimental

# Conflicts:
#	.devops/intel.Dockerfile
#	.devops/nix/package.nix
#	.gitignore
#	docs/backend/SYCL.md
#	docs/ops.md
#	docs/ops/SYCL.csv
#	ggml/CMakeLists.txt
#	ggml/src/ggml-cuda/fattn.cu
#	ggml/src/ggml-cuda/ggml-cuda.cu
#	ggml/src/ggml-sycl/CMakeLists.txt
#	ggml/src/ggml-sycl/common.hpp
#	ggml/src/ggml-sycl/convert.cpp
#	ggml/src/ggml-sycl/dequantize.hpp
#	ggml/src/ggml-sycl/fattn-common.hpp
#	ggml/src/ggml-sycl/getrows.cpp
#	ggml/src/ggml-sycl/ggml-sycl.cpp
#	ggml/src/ggml-sycl/im2col.cpp
#	ggml/src/ggml-sycl/im2col.hpp
#	ggml/src/ggml-sycl/mmvq.cpp
#	ggml/src/ggml-sycl/quants.hpp
#	ggml/src/ggml-sycl/vecdotq.hpp
#	ggml/src/ggml-virtgpu/ggml-backend-device.cpp
#	scripts/sync-ggml.last
#	scripts/sync_vendor.py
#	tests/test-backend-ops.cpp

src/llama-model.cpp

@@ -1261,10 +1261,6 @@ void llama_model_base::load_hparams(llama_model_loader & ml) {
         ml.get_key(LLM_KV_ROPE_DIMENSION_COUNT_SWA, hparams.n_rot_swa, false);
     }
 
-    // for differentiating model types
-    uint32_t n_vocab = 0;
-    ml.get_key(LLM_KV_VOCAB_SIZE, n_vocab, false) || ml.get_arr_n(LLM_KV_TOKENIZER_LIST, n_vocab, false);
-
     // for classifier models
     ml.get_arr(LLM_KV_CLASSIFIER_OUTPUT_LABELS, classifier_labels, false);
     if (!classifier_labels.empty()) {

src/llama-vocab.cpp

@@ -728,6 +728,14 @@ struct llm_tokenizer_bpe : llm_tokenizer {
                 };
                 byte_encode = false; // uses raw UTF-8, not GPT-2 byte encoding
                 break;
+            case LLAMA_VOCAB_PRE_TYPE_SARVAM_MOE:
+                // Sarvam uses SPM-style BPE (same shape as Gemma4): spaces replaced with U+2581
+                // by the normalizer, BPE merges over the whole text on raw UTF-8.
+                regex_exprs = {
+                    "[^\\n]+|[\\n]+",
+                };
+                byte_encode = false;
+                break;
             default:
                 // default regex for BPE tokenization pre-processing
                 regex_exprs = {
@@ -2241,6 +2249,11 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
                     tokenizer_pre == "gemma4") {
                 pre_type = LLAMA_VOCAB_PRE_TYPE_GEMMA4;
                 escape_whitespaces = true;
+            } else if (
+                    tokenizer_pre == "sarvam-moe") {
+                pre_type = LLAMA_VOCAB_PRE_TYPE_SARVAM_MOE;
+                escape_whitespaces = true;
+                clean_spaces = false;
             } else if (
                     tokenizer_pre == "jina-v1-en" ||
                     tokenizer_pre == "jina-v2-code" ||

src/llama-vocab.h

@@ -60,6 +60,7 @@ enum llama_vocab_pre_type {
     LLAMA_VOCAB_PRE_TYPE_JOYAI_LLM       = 48,
     LLAMA_VOCAB_PRE_TYPE_JAIS2           = 49,
     LLAMA_VOCAB_PRE_TYPE_GEMMA4          = 50,
+    LLAMA_VOCAB_PRE_TYPE_SARVAM_MOE      = 51,
 };
 
 struct LLM_KV;

src/models/deepseek2.cpp

@@ -1,7 +1,8 @@
 #include "models.h"
 
 void llama_model_deepseek2::load_arch_hparams(llama_model_loader & ml) {
-    const auto n_vocab = vocab.n_tokens();
+    uint32_t n_vocab = 0;
+    ml.get_key(LLM_KV_VOCAB_SIZE, n_vocab, false) || ml.get_arr_n(LLM_KV_TOKENIZER_LIST, n_vocab, false);
 
     // lite variants include DeepSeek-V2-Lite, GigaChat3-10B-A1.8B, Kanana-2-30B-A3B
     const bool is_lite = (hparams.n_layer == 27 || hparams.n_layer == 26 || (hparams.n_layer == 48 && n_vocab == 128256));

src/models/llama.cpp

@@ -1,7 +1,8 @@
 #include "models.h"
 
 void llama_model_llama::load_arch_hparams(llama_model_loader & ml) {
-    const auto n_vocab = vocab.n_tokens();
+    uint32_t n_vocab = 0;
+    ml.get_key(LLM_KV_VOCAB_SIZE, n_vocab, false) || ml.get_arr_n(LLM_KV_TOKENIZER_LIST, n_vocab, false);
 
     ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);