model : add sarvam_moe architecture support (#20275)

convert_hf_to_gguf.py

@@ -1570,6 +1570,9 @@ class TextModel(ModelBase):
         if chkhsh == "862f827721df956049dff5ca81a57f29e575280bc622e290d3bf4e35eca29015":
             # ref: https://huggingface.co/codefuse-ai/F2LLM-v2-4B
             res = "f2llmv2"
+        if chkhsh == "62f6fb0a6fd5098caeabb19b07a5c1099cafc8b9c40eab6ea89ece4ec02fbc57":
+            # ref: https://huggingface.co/sarvamai/sarvam-30b
+            res = "sarvam-moe"
 
         if res is None:
             logger.warning("\n")
@@ -11591,6 +11594,34 @@ class BailingMoeV2Model(TextModel):
                 raise ValueError(f"Unprocessed experts: {experts}")
 
 
+@ModelBase.register("SarvamMoEForCausalLM", "modeling_sarvam_moe.SarvamMoEForCausalLM")
+class SarvamMoEModel(BailingMoeV2Model):
+    model_arch = gguf.MODEL_ARCH.BAILINGMOE2
+    # Sarvam-MoE shares the BailingMoeV2 architecture; only differences:
+    #  - full rotary (no partial_rotary_factor)
+    #  - expert bias is zero-mean normalized at load time
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        hparams = self.hparams
+        if (rope_dim := hparams.get("head_dim")) is None:
+            rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
+        # Override the partial-rotary value written by BailingMoeV2 with the full rotary dim
+        self.gguf_writer.add_rope_dimension_count(rope_dim)
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+        if name.endswith(".expert_bias"):
+            # Sarvam normalizes expert bias to zero mean
+            inner = gen
+
+            def gen():
+                t = inner()
+                return t - t.mean()
+        return super().filter_tensors((name, gen))
+
+
 @ModelBase.register("GroveMoeForCausalLM", "modeling_grove_moe.GroveMoeForCausalLM")
 class GroveMoeModel(TextModel):
     model_arch = gguf.MODEL_ARCH.GROVEMOE

convert_hf_to_gguf_update.py

@@ -155,6 +155,7 @@ models = [
     {"name": "joyai-llm",        "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jdopensource/JoyAI-LLM-Flash", },
     {"name": "kanana2",          "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/kakaocorp/kanana-2-30b-a3b-instruct-2601", },
     {"name": "f2llmv2",          "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/codefuse-ai/F2LLM-v2-4B", },
+    {"name": "sarvam-moe",       "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/sarvamai/sarvam-30b", },
 ]
 
 # some models are known to be broken upstream, so we will skip them as exceptions

src/llama-vocab.cpp

@@ -503,6 +503,14 @@ 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 = {
@@ -2005,6 +2013,11 @@ 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

@@ -59,6 +59,7 @@ 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;