ikawrakow · ikawrakow · Sep 23, 2025 · Sep 16, 2025 · ikawrakow · Sep 23, 2025
diff --git a/common/common.h b/common/common.h
@@ -116,9 +116,9 @@ struct gpt_params {
     float   rope_freq_base        =  0.0f; // RoPE base frequency
     float   rope_freq_scale       =  0.0f; // RoPE frequency scaling factor
     float   yarn_ext_factor       = -1.0f; // YaRN extrapolation mix factor
-    float   yarn_attn_factor      =  1.0f; // YaRN magnitude scaling factor
-    float   yarn_beta_fast        = 32.0f; // YaRN low correction dim
-    float   yarn_beta_slow        =  1.0f; // YaRN high correction dim
+    float   yarn_attn_factor      =  -1.0f; // YaRN magnitude scaling factor
+    float   yarn_beta_fast        = -1.0f; // YaRN low correction dim
+    float   yarn_beta_slow        =  -1.0f; // YaRN high correction dim
     int32_t yarn_orig_ctx         =     0; // YaRN original context length
     float   defrag_thold          = -1.0f; // KV cache defragmentation threshold
 

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
@@ -555,6 +555,9 @@ def get_vocab_base_pre(self, tokenizer) -> str:
         # NOTE: if you get an error here, you need to update the convert_hf_to_gguf_update.py script
         #       or pull the latest version of the model from Huggingface
         #       don't edit the hashes manually!
+        if chkhsh == "66b8d4e19ab16c3bfd89bce5d785fb7e0155e8648708a1f42077cb9fe002c273":
+            # ref: https://huggingface.co/alvarobartt/grok-2-tokenizer
+            res = "grok-2"
         if chkhsh == "0ef9807a4087ebef797fc749390439009c3b9eda9ad1a097abbe738f486c01e5":
             # ref: https://huggingface.co/meta-llama/Meta-Llama-3-8B
             res = "llama-bpe"
@@ -1905,57 +1908,109 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter
         return tensors
 
 
-@Model.register("GrokForCausalLM")
+@Model.register("GrokForCausalLM", "Grok1ForCausalLM")
 class GrokModel(Model):
     model_arch = gguf.MODEL_ARCH.GROK
 
     def set_vocab(self):
-        self._set_vocab_sentencepiece()
+        if (self.dir_model / 'tokenizer.model').is_file():
+            self._set_vocab_sentencepiece()
+            return
+
+        if not (self.dir_model / 'tokenizer.json').is_file() or not (self.dir_model / 'chat_template.jinja').is_file():
+            logger.error('Error: Missing vocab and chat template, download files from https://huggingface.co/alvarobartt/grok-2-tokenizer')
+            sys.exit(1)
+
+        self._set_vocab_gpt2()
 
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
 
     def set_gguf_parameters(self):
         super().set_gguf_parameters()
 
-    _experts: list[dict[str, Tensor]] | None = None
+        self.gguf_writer.add_attn_logit_softcapping(self.hparams.get("attn_logit_softcapping", 30.0))
+        self.gguf_writer.add_router_logit_softcapping(self.hparams.get("router_logit_softcapping", 30.0))
+        if (final_logit_softcap := self.hparams.get("final_logit_softcapping")):
+            self.gguf_writer.add_final_logit_softcapping(final_logit_softcap)
+
+        if (rope_dim := self.hparams.get("head_dim")) is None:
+            rope_dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
+
+        if (moe_intermediate_size := self.hparams.get("moe_intermediate_size")) is not None:
+            self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
+
+        # Treat "original" as "yarn", seems to have been a mistake
+        if self.hparams.get("rope_type") in ("yarn", "original"):
+            self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN)
+            self.gguf_writer.add_rope_scaling_factor(self.hparams["scaling_factor"])
+            self.gguf_writer.add_rope_scaling_orig_ctx_len(self.hparams["original_max_position_embeddings"])
+            self.gguf_writer.add_rope_scaling_yarn_ext_factor(self.hparams["extrapolation_factor"])
+            self.gguf_writer.add_rope_scaling_yarn_attn_factor(self.hparams["attn_factor"])
+            self.gguf_writer.add_rope_scaling_yarn_beta_fast(self.hparams["beta_fast"])
+            self.gguf_writer.add_rope_scaling_yarn_beta_slow(self.hparams["beta_slow"])
+
+        if temp_len := self.hparams.get("attn_temperature_len"):
+            self.gguf_writer.add_attn_temperature_length(temp_len)
+
+        self.gguf_writer.add_attn_output_scale(self.hparams.get("attn_output_multiplier", rope_dim**-0.5))
+        self.gguf_writer.add_embedding_scale(self.hparams["embedding_multiplier_scale"])
+        self.gguf_writer.add_logit_scale(self.hparams["output_multiplier_scale"])
+
+    _experts: list[dict[str, list[Tensor]]] | None = None
+    _cur_expert = ""
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        tensors: list[tuple[str, Tensor]] = []
+        is_expert = ".moe." in name or ".block_sparse_moe.experts." in name
+
+        if not is_expert:
+            tensors.append((self.map_tensor_name(name), data_torch))
+
         # process the experts separately
-        if name.find(".moe.") != -1:
+        if is_expert or self._cur_expert:
             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:
-                tensors: list[tuple[str, Tensor]] = []
+            # concatenate split tensors
+            if name in self._experts[bid]:
+                self._cur_expert = name
+                self._experts[bid][name].append(data_torch)
+                return []
+            elif is_expert:
+                self._cur_expert = name
+                self._experts[bid][name] = [data_torch]
+                return []
+            else:
+                self._cur_expert = ""
 
-                # merge the experts into a single 3d tensor
-                for wid in ["linear", "linear_1", "linear_v"]:
-                    datas: list[Tensor] = []
+            for bid in range(self.block_count):
+                if len(self._experts[bid]) >= n_experts * 3:
+                    # merge the experts into a single 3d tensor
+                    for wid in [("linear", "w1", 0), ("linear_1", "w2", 1), ("linear_v", "w3", 0)]:
+                        datas: list[Tensor] = []
 
-                    for xid in range(n_experts):
-                        ename = f"transformer.decoder_layer.{bid}.moe.{xid}.{wid}.weight"
-                        datas.append(self._experts[bid][ename])
-                        del self._experts[bid][ename]
+                        for xid in range(n_experts):
+                            ename = f"transformer.decoder_layer.{bid}.moe.{xid}.{wid[0]}.weight"
+                            if ename not in self._experts[bid]:
+                                ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{wid[1]}.weight"
+                            tensor_list = self._experts[bid][ename]
+                            datas.append(torch.cat(tensor_list, dim=wid[2]) if len(tensor_list) > 1 else tensor_list[0])
+                            del self._experts[bid][ename]
 
-                    data_torch = torch.stack(datas, dim=0)
+                        data_torch = torch.stack(datas, dim=0)
 
-                    merged_name = f"transformer.decoder_layer.{bid}.moe.{wid}.weight"
+                        merged_name = f"transformer.decoder_layer.{bid}.moe.{wid[0]}.weight"
 
-                    new_name = self.map_tensor_name(merged_name)
+                        new_name = self.map_tensor_name(merged_name)
 
-                    tensors.append((new_name, data_torch))
-                return tensors
-            else:
-                return []
+                        yield (new_name, data_torch)
 
-        return [(self.map_tensor_name(name), data_torch)]
+        yield from tensors
 
 
 @Model.register("DbrxForCausalLM")

diff --git a/convert_hf_to_gguf_update.py b/convert_hf_to_gguf_update.py
@@ -99,6 +99,7 @@ class TOKENIZER_TYPE(IntEnum):
     {"name": "glm4",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/THUDM/glm-4-9b-hf",       "chkhsh": "a1336059768a55c99a734006ffb02203cd450fed003e9a71886c88acf24fdbc2", },
     {"name": "glm4",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/zai-org/GLM-4.5-Air",     "chkhsh": "9ca2dd618e8afaf09731a7cf6e2105b373ba6a1821559f258b272fe83e6eb902", },
     {"name": "kimi-k2",        "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/moonshotai/Kimi-K2-Base", "chkhsh": "81212dc7cdb7e0c1074ca62c5aeab0d43c9f52b8a737be7b12a777c953027890", },
+    {"name": "grok-2",         "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/alvarobartt/grok-2-tokenizer", "chkhsh": "66b8d4e19ab16c3bfd89bce5d785fb7e0155e8648708a1f42077cb9fe002c273"},
 ]
 
 

diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py
@@ -97,6 +97,7 @@ class LLM:
         DECODER_START_TOKEN_ID            = "{arch}.decoder_start_token_id"
         ATTN_LOGIT_SOFTCAPPING            = "{arch}.attn_logit_softcapping"
         FINAL_LOGIT_SOFTCAPPING           = "{arch}.final_logit_softcapping"
+        ROUTER_LOGIT_SOFTCAPPING          = "{arch}.router_logit_softcapping"
 
     class Attention:
         HEAD_COUNT        = "{arch}.attention.head_count"
@@ -112,16 +113,22 @@ class Attention:
         KV_LORA_RANK      = "{arch}.attention.kv_lora_rank"
         REL_BUCKETS_COUNT = "{arch}.attention.relative_buckets_count"
         SLIDING_WINDOW    = "{arch}.attention.sliding_window"
+        OUTPUT_SCALE                 = "{arch}.attention.output_scale"
+        TEMPERATURE_LENGTH           = "{arch}.attention.temperature_length"
 
     class Rope:
-        DIMENSION_COUNT         = "{arch}.rope.dimension_count"
-        FREQ_BASE               = "{arch}.rope.freq_base"
-        SCALING_TYPE            = "{arch}.rope.scaling.type"
-        SCALING_FACTOR          = "{arch}.rope.scaling.factor"
-        SCALING_ATTN_FACTOR     = "{arch}.rope.scaling.attn_factor"
-        SCALING_ORIG_CTX_LEN    = "{arch}.rope.scaling.original_context_length"
-        SCALING_FINETUNED       = "{arch}.rope.scaling.finetuned"
-        SCALING_YARN_LOG_MUL    = "{arch}.rope.scaling.yarn_log_multiplier"
+        DIMENSION_COUNT          = "{arch}.rope.dimension_count"
+        FREQ_BASE                = "{arch}.rope.freq_base"
+        SCALING_TYPE             = "{arch}.rope.scaling.type"
+        SCALING_FACTOR           = "{arch}.rope.scaling.factor"
+        SCALING_ATTN_FACTOR      = "{arch}.rope.scaling.attn_factor"
+        SCALING_ORIG_CTX_LEN     = "{arch}.rope.scaling.original_context_length"
+        SCALING_FINETUNED        = "{arch}.rope.scaling.finetuned"
+        SCALING_YARN_LOG_MUL     = "{arch}.rope.scaling.yarn_log_multiplier"
+        SCALING_YARN_EXT_FACTOR  = "{arch}.rope.scaling.yarn_ext_factor"
+        SCALING_YARN_ATTN_FACTOR = "{arch}.rope.scaling.yarn_attn_factor"
+        SCALING_YARN_BETA_FAST   = "{arch}.rope.scaling.yarn_beta_fast"
+        SCALING_YARN_BETA_SLOW   = "{arch}.rope.scaling.yarn_beta_slow"
 
     class Split:
         LLM_KV_SPLIT_NO            = "split.no"
@@ -540,6 +547,7 @@ class MODEL_TENSOR(IntEnum):
         MODEL_TENSOR.FFN_GATE_EXP,
         MODEL_TENSOR.FFN_DOWN_EXP,
         MODEL_TENSOR.FFN_UP_EXP,
+        MODEL_TENSOR.FFN_POST_NORM,
         MODEL_TENSOR.LAYER_OUT_NORM,
     ],
     MODEL_ARCH.GPTNEOX: [

diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py
@@ -656,6 +656,9 @@ def add_logit_scale(self, value: float) -> None:
     def add_attn_logit_softcapping(self, value: float) -> None:
         self.add_float32(Keys.LLM.ATTN_LOGIT_SOFTCAPPING.format(arch=self.arch), value)
 
+    def add_router_logit_softcapping(self, value: float) -> None:
+        self.add_float32(Keys.LLM.ROUTER_LOGIT_SOFTCAPPING.format(arch=self.arch), value)
+
     def add_final_logit_softcapping(self, value: float) -> None:
         self.add_float32(Keys.LLM.FINAL_LOGIT_SOFTCAPPING.format(arch=self.arch), value)
 
@@ -701,6 +704,12 @@ def add_relative_attn_buckets_count(self, value: int) -> None:
     def add_sliding_window(self, value: int) -> None:
         self.add_uint32(Keys.Attention.SLIDING_WINDOW.format(arch=self.arch), value)
 
+    def add_attn_output_scale(self, value: float) -> None:
+        self.add_float32(Keys.Attention.OUTPUT_SCALE.format(arch=self.arch), value)
+
+    def add_attn_temperature_length(self, value: int) -> None:
+        self.add_uint32(Keys.Attention.TEMPERATURE_LENGTH.format(arch=self.arch), value)
+
     def add_pooling_type(self, value: PoolingType) -> None:
         self.add_uint32(Keys.LLM.POOLING_TYPE.format(arch=self.arch), value.value)
 
@@ -728,6 +737,18 @@ def add_rope_scaling_finetuned(self, value: bool) -> None:
     def add_rope_scaling_yarn_log_mul(self, value: float) -> None:
         self.add_float32(Keys.Rope.SCALING_YARN_LOG_MUL.format(arch=self.arch), value)
 
+    def add_rope_scaling_yarn_ext_factor(self, value: float) -> None:
+        self.add_float32(Keys.Rope.SCALING_YARN_EXT_FACTOR.format(arch=self.arch), value)
+
+    def add_rope_scaling_yarn_attn_factor(self, value: float) -> None:
+        self.add_float32(Keys.Rope.SCALING_YARN_ATTN_FACTOR.format(arch=self.arch), value)
+
+    def add_rope_scaling_yarn_beta_fast(self, value: float) -> None:
+        self.add_float32(Keys.Rope.SCALING_YARN_BETA_FAST.format(arch=self.arch), value)
+
+    def add_rope_scaling_yarn_beta_slow(self, value: float) -> None:
+        self.add_float32(Keys.Rope.SCALING_YARN_BETA_SLOW.format(arch=self.arch), value)
+
     def add_ssm_conv_kernel(self, value: int) -> None:
         self.add_uint32(Keys.SSM.CONV_KERNEL.format(arch=self.arch), value)
 

diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py
@@ -24,6 +24,7 @@ class TensorNameMap:
             "backbone.embedding",                        # mamba
             "backbone.embeddings",                       # mamba-hf
             "transformer.in_out_embed",                  # Grok
+            "model.layers.{bid}.pre_attn_norm",          # grok-2
             "embedding.word_embeddings",                 # chatglm
             "transformer.token_embeddings",              # openelm
             "shared",                                    # t5
@@ -202,6 +203,7 @@ class TensorNameMap:
             "encoder.layer.{bid}.attention.output.LayerNorm",  # bert
             "encoder.layers.{bid}.norm1",                      # nomic-bert
             "transformer.decoder_layer.{bid}.rms_norm_1",      # Grok
+            "model.layers.{bid}.post_attn_norm",               # grok-2
             "transformer.blocks.{bid}.norm_attn_norm.norm_2",  # dbrx
         ),
 
@@ -230,6 +232,7 @@ class TensorNameMap:
             "h.{bid}.ln_2",                                                  # gpt2
             "model.layers.{bid}.ffn_norm",                                   # internlm2
             "transformer.decoder_layer.{bid}.rms_norm_2",                    # Grok
+            "model.layers.{bid}.pre_moe_norm",                               # grok-2
             "encoder.layers.{bid}.post_attention_layernorm",                 # chatglm
             "transformer.layers.{bid}.ffn_norm",                             # openelm
         ),
@@ -242,6 +245,7 @@ class TensorNameMap:
         # Post feed-forward norm
         MODEL_TENSOR.FFN_POST_NORM: (
             "model.layers.{bid}.post_feedforward_layernorm", # gemma2
+            "model.layers.{bid}.post_moe_norm",              # grok-2
         ),
 
         MODEL_TENSOR.FFN_GATE_INP: (

diff --git a/src/llama-arch.h b/src/llama-arch.h
@@ -99,6 +99,7 @@ enum llm_kv {
     LLM_KV_LOGIT_SCALE,
     LLM_KV_DECODER_START_TOKEN_ID,
     LLM_KV_ATTN_LOGIT_SOFTCAPPING,
+    LLM_KV_ROUTER_LOGIT_SOFTCAPPING,
     LLM_KV_FINAL_LOGIT_SOFTCAPPING,
     LLM_KV_SWIN_NORM,
     LLM_KV_RESCALE_EVERY_N_LAYERS,
@@ -123,7 +124,8 @@ enum llm_kv {
     LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT,
     LLM_KV_ATTENTION_SLIDING_WINDOW,
     LLM_KV_ATTENTION_SCALE,
-
+    LLM_KV_ATTENTION_OUTPUT_SCALE,
+    LLM_KV_ATTENTION_TEMPERATURE_LENGTH,
     LLM_KV_ROPE_DIMENSION_COUNT,
     LLM_KV_ROPE_FREQ_BASE,
     LLM_KV_ROPE_SCALE_LINEAR,
@@ -134,6 +136,11 @@ enum llm_kv {
     LLM_KV_ROPE_SCALING_FINETUNED,
     LLM_KV_ROPE_SCALING_YARN_LOG_MUL,
 
+    LLM_KV_ROPE_SCALING_YARN_EXT_FACTOR,
+    LLM_KV_ROPE_SCALING_YARN_ATTN_FACTOR,
+    LLM_KV_ROPE_SCALING_YARN_BETA_FAST,
+    LLM_KV_ROPE_SCALING_YARN_BETA_SLOW,
+
     LLM_KV_SPLIT_NO,
     LLM_KV_SPLIT_COUNT,
     LLM_KV_SPLIT_TENSORS_COUNT,

diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp
@@ -433,6 +433,13 @@ struct llm_tokenizer_bpe : llm_tokenizer {
                     "(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1}| ?[^\\s\\p{L}\\p{N}\\r\\n]+|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
                 };
                 break;
+            case LLAMA_VOCAB_PRE_TYPE_GROK_2:
+                regex_exprs = {
+                    // original regex from tokenizer.json
+                    // "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+"
+                    "(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
+                };
+                break;
             default:
                 // default regex for BPE tokenization pre-processing
                 regex_exprs = {
@@ -1973,6 +1980,11 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
                 tokenizer_pre == "kimi-k2") {
                 pre_type = LLAMA_VOCAB_PRE_TYPE_KIMI_K2;
                 clean_spaces = false;
+            }
+            else if (
+                tokenizer_pre == "grok-2") {
+                pre_type = LLAMA_VOCAB_PRE_TYPE_GROK_2;
+                clean_spaces = false;
             } else {
                 throw std::runtime_error(format("unknown pre-tokenizer type: '%s'", tokenizer_pre.c_str()));
             }

diff --git a/src/llama-vocab.h b/src/llama-vocab.h
@@ -47,6 +47,7 @@ enum llama_vocab_pre_type {
     LLAMA_VOCAB_PRE_TYPE_HUNYUAN        = 36,
     LLAMA_VOCAB_PRE_TYPE_KIMI_K2        = 37,
     LLAMA_VOCAB_PRE_TYPE_HUNYUAN_DENSE  = 38,
+    LLAMA_VOCAB_PRE_TYPE_GROK_2         = 39,
 };
 
 struct LLM_KV;