Glm 4.5

convert_hf_to_gguf.py

@@ -678,6 +678,9 @@ def get_vocab_base_pre(self, tokenizer) -> str:
         if chkhsh == "a1336059768a55c99a734006ffb02203cd450fed003e9a71886c88acf24fdbc2":
             # ref: https://huggingface.co/THUDM/glm-4-9b-hf
             res = "glm4"
+        if chkhsh == "9ca2dd618e8afaf09731a7cf6e2105b373ba6a1821559f258b272fe83e6eb902":
+            # ref: https://huggingface.co/zai-org/GLM-4.5-Air
+            res = "glm4"
         if chkhsh == "1431a23e583c97432bc230bff598d103ddb5a1f89960c8f1d1051aaa944d0b35":
             # ref: https://huggingface.co/sapienzanlp/Minerva-7B-base-v1.0
             res = "minerva-7b"
@@ -6685,6 +6688,149 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter
         return super().modify_tensors(data_torch, name, bid)
 
 
+@ModelBase.register("Glm4MoeForCausalLM")
+class Glm4MoeModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.GLM4_MOE
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        # GLM4_MOE has num_hidden_layers + 1 actual layers (including NextN layer)
+        self.block_count = self.hparams["num_hidden_layers"] + 1
+        self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
+
+    def set_vocab(self):
+        from transformers import AutoTokenizer
+
+        tokenizer = AutoTokenizer.from_pretrained(self.dir_model)
+        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
+        tokens, toktypes, tokpre = self.get_vocab_base()
+        self.gguf_writer.add_tokenizer_model("gpt2")
+        self.gguf_writer.add_tokenizer_pre(tokpre)
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_types(toktypes)
+
+        # Special tokens
+        # Note: Using <|endoftext|> (151329) for eos and eot causes endless generation
+        special_vocab._set_special_token("bos", tokenizer.get_added_vocab()["[gMASK]"])  # 151331
+        special_vocab._set_special_token("eos", tokenizer.get_added_vocab()["<|user|>"])  # 151336 - end of
+        special_vocab._set_special_token("eot", tokenizer.get_added_vocab()["<|user|>"])  # 151336 - same as EOS
+        special_vocab._set_special_token("eog", tokenizer.get_added_vocab()["<|user|>"])  # 151336 - same as EOS
+        special_vocab._set_special_token("unk", tokenizer.get_added_vocab()["<|endoftext|>"]) # 151329
+        special_vocab._set_special_token("eom", tokenizer.get_added_vocab()["<|observation|>"])  # 151338
+
+        if "<sop>" in tokenizer.get_added_vocab():
+            special_vocab._set_special_token("sop", tokenizer.get_added_vocab()["<sop>"])  # 151333
+        if "<eop>" in tokenizer.get_added_vocab():
+            special_vocab._set_special_token("eop", tokenizer.get_added_vocab()["<eop>"])  # 151334
+
+        special_vocab.add_to_gguf(self.gguf_writer)
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        if (rope_dim := self.hparams.get("head_dim")) is None:
+            rope_dim = (
+                self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
+            )
+        self.gguf_writer.add_rope_dimension_count(
+            int(rope_dim * self.hparams.get("partial_rotary_factor", 0.5))
+        )
+
+        # MoE parameters - Use only routed expert count (shared experts handled separately)
+        if (n_routed_experts := self.hparams.get("n_routed_experts")) is not None:
+            self.gguf_writer.add_expert_count(n_routed_experts)
+        if (num_experts_per_tok := self.hparams.get("num_experts_per_tok")) is not None:
+            self.gguf_writer.add_expert_used_count(num_experts_per_tok)
+        if (moe_intermediate_size := self.hparams.get("moe_intermediate_size")) is not None:
+            self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
+        if (n_shared_experts := self.hparams.get("n_shared_experts")) is not None:
+            self.gguf_writer.add_expert_shared_count(n_shared_experts)
+        if (first_k_dense_replace := self.hparams.get("first_k_dense_replace")) is not None:
+            self.gguf_writer.add_leading_dense_block_count(first_k_dense_replace)
+
+        # Expert gating function (sigmoid for GLM4_MOE)
+        self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID)
+
+        # Routed scaling factor
+        if (routed_scaling_factor := self.hparams.get("routed_scaling_factor")) is not None:
+            self.gguf_writer.add_expert_weights_scale(routed_scaling_factor)
+
+        # Normalise topk probabilities
+        if (norm_topk_prob := self.hparams.get("norm_topk_prob")) is not None:
+            self.gguf_writer.add_expert_weights_norm(norm_topk_prob)
+
+    _experts: list[dict[str, Tensor]] | None = None
+
+    def modify_tensors(
+        self, data_torch: Tensor, name: str, bid: int | None
+    ) -> Iterable[tuple[str, Tensor]]:
+        if name.startswith("model.visual."):  # ignore visual part
+            return []
+        elif name.startswith("model.language_model."):
+            name = name.replace("language_model.", "")  # for multimodal variants
+
+        # Handle main token embedding (but not layer-specific NextN embeddings)
+        if name == "model.embed_tokens.weight" and ".layers." not in name:
+            return [(self.map_tensor_name("token_embd.weight"), data_torch)]
+
+        # Handle routed experts
+        if name.find("mlp.experts") != -1:
+            n_experts = self.hparams["n_routed_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]] = []
+
+                # merge the experts into a single 3d tensor
+                for w_name in ["down_proj", "gate_proj", "up_proj"]:
+                    datas: list[Tensor] = []
+
+                    for xid in range(n_experts):
+                        ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
+                        datas.append(self._experts[bid][ename])
+                        del self._experts[bid][ename]
+
+                    data_torch = torch.stack(datas, dim=0)
+
+                    merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
+
+                    new_name = self.map_tensor_name(merged_name)
+                    tensors.append((new_name, data_torch))
+                return tensors
+            else:
+                return []
+
+        if name.endswith("e_score_correction_bias"):
+            name = name.replace("e_score_correction_bias", "e_score_correction.bias")
+
+        # Handle special NextN tensors - preserve for future MTP support
+        if (
+            ".embed_tokens." in name
+            or ".shared_head." in name
+            or ".eh_proj." in name
+            or ".enorm." in name
+            or ".hnorm." in name
+        ):
+            new_name = name.replace("model.layers.", "blk.").replace("model.", "").replace(".weight", "")
+            return [(new_name, data_torch)]
+
+        new_name = self.map_tensor_name(name)
+
+        return [(new_name, data_torch)]
+
+    def prepare_tensors(self):
+        super().prepare_tensors()
+        if self._experts is not None:
+            # flatten `list[dict[str, Tensor]]` into `list[str]`
+            experts = [k for d in self._experts for k in d.keys()]
+            if len(experts) > 0:
+                raise ValueError(f"Unprocessed experts: {experts}")
+
+
 @ModelBase.register("GlmForCausalLM", "ChatGLMModel", "ChatGLMForConditionalGeneration")
 class ChatGLMModel(TextModel):
     model_arch = gguf.MODEL_ARCH.CHATGLM
@@ -6701,7 +6847,7 @@ def set_vocab_chatglm3(self):
         vocab_size = hparams.get("padded_vocab_size", len(tokenizer.get_vocab()))
         assert max(tokenizer.get_vocab().values()) < vocab_size
         role_special_tokens = ["<|system|>", "<|user|>", "<|assistant|>", "<|observation|>"]
-        special_tokens = ["[MASK]", "[gMASK]", "[sMASK]", "sop", "eop"] + role_special_tokens
+        special_tokens = ["[MASK]", "[gMASK]", "sop", "eop"] + role_special_tokens
         for token_id in range(vocab_size):
             piece = tokenizer._convert_id_to_token(token_id)
             if token_id == 0:

convert_hf_to_gguf_update.py

@@ -138,6 +138,7 @@ class TOKENIZER_TYPE(IntEnum):
     {"name": "chatglm-bpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/THUDM/glm-4-9b-chat", "chkhsh": "b6e8e1518dc4305be2fe39c313ed643381c4da5db34a98f6a04c093f8afbe99b"},
     {"name": "chatglm-bpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/THUDM/glm-4-9b-chat", "chkhsh": "81d72c7348a9f0ebe86f23298d37debe0a5e71149e29bd283904c02262b27516"},
     {"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": "minerva-7b", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/sapienzanlp/Minerva-7B-base-v1.0", "chkhsh": "1431a23e583c97432bc230bff598d103ddb5a1f89960c8f1d1051aaa944d0b35"},
     {"name": "hunyuan", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tencent/Hunyuan-A13B-Instruct", "chkhsh": "7e57df22b1fe23a7b1e1c7f3dc4e3f96d43a4eb0836d0c6bdc3436d7b2f1c664"},
     {"name": "hunyuan-dense", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tencent/Hunyuan-4B-Instruct", "chkhsh": "bba3b3366b646dbdded5dbc42d59598b849371afc42f7beafa914afaa5b70aa6"},

gguf-py/gguf/constants.py

@@ -357,6 +357,7 @@ class MODEL_ARCH(IntEnum):
     DEEPSEEK2        = auto()
     CHATGLM          = auto()
     GLM4             = auto()
+    GLM4_MOE         = auto()
     BITNET           = auto()
     T5               = auto()
     T5ENCODER        = auto()
@@ -614,6 +615,12 @@ class MODEL_TENSOR(IntEnum):
     A_MMPROJ_FC          = auto()
     A_MM_NORM_PRE        = auto()
     A_MM_NORM_MID        = auto()
+    NEXTN_EH_PROJ        = auto()   # nextn tensors (glm4moe)
+    NEXTN_EMBED_TOKENS   = auto()   # nextn tensors (glm4moe)
+    NEXTN_ENORM          = auto()   # nextn tensors (glm4moe)
+    NEXTN_HNORM          = auto()   # nextn tensors (glm4moe)
+    NEXTN_SHARED_HEAD_HEAD = auto() # nextn tensors (glm4moe)
+    NEXTN_SHARED_HEAD_NORM = auto() # nextn tensors (glm4moe)
 
 
 MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
@@ -678,6 +685,7 @@ class MODEL_TENSOR(IntEnum):
     MODEL_ARCH.DEEPSEEK2:        "deepseek2",
     MODEL_ARCH.CHATGLM:          "chatglm",
     MODEL_ARCH.GLM4:             "glm4",
+    MODEL_ARCH.GLM4_MOE:         "glm4moe",
     MODEL_ARCH.BITNET:           "bitnet",
     MODEL_ARCH.T5:               "t5",
     MODEL_ARCH.T5ENCODER:        "t5encoder",
@@ -936,6 +944,13 @@ class MODEL_TENSOR(IntEnum):
     MODEL_TENSOR.A_MMPROJ_FC:               "mm.a.fc",
     MODEL_TENSOR.A_MM_NORM_PRE:             "mm.a.norm_pre",
     MODEL_TENSOR.A_MM_NORM_MID:             "mm.a.norm_mid",
+    # NextN/MTP tensors (GLM4_MOE)
+    MODEL_TENSOR.NEXTN_EH_PROJ:             "blk.{bid}.eh_proj",
+    MODEL_TENSOR.NEXTN_EMBED_TOKENS:        "blk.{bid}.embed_tokens",
+    MODEL_TENSOR.NEXTN_ENORM:               "blk.{bid}.enorm",
+    MODEL_TENSOR.NEXTN_HNORM:               "blk.{bid}.hnorm",
+    MODEL_TENSOR.NEXTN_SHARED_HEAD_HEAD:    "blk.{bid}.shared_head.head",
+    MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM:    "blk.{bid}.shared_head.norm",
 }
 
 MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
@@ -2124,6 +2139,37 @@ class MODEL_TENSOR(IntEnum):
         MODEL_TENSOR.ATTN_POST_NORM,
         MODEL_TENSOR.FFN_POST_NORM,
     ],
+    MODEL_ARCH.GLM4_MOE: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_POST_NORM,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.ATTN_Q_NORM,
+        MODEL_TENSOR.ATTN_K_NORM,
+        MODEL_TENSOR.FFN_GATE,          # dense layers
+        MODEL_TENSOR.FFN_DOWN,          # dense layers
+        MODEL_TENSOR.FFN_UP,            # dense layers
+        MODEL_TENSOR.FFN_GATE_INP,
+        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_EXP_PROBS_B,
+        # NextN/MTP tensors - preserved but unused
+        MODEL_TENSOR.NEXTN_EH_PROJ,
+        MODEL_TENSOR.NEXTN_EMBED_TOKENS,
+        MODEL_TENSOR.NEXTN_ENORM,
+        MODEL_TENSOR.NEXTN_HNORM,
+        MODEL_TENSOR.NEXTN_SHARED_HEAD_HEAD,
+        MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM,
+    ],
     MODEL_ARCH.BITNET: [
         MODEL_TENSOR.ATTN_Q,
         MODEL_TENSOR.ATTN_K,

models/templates/README.md

@@ -21,4 +21,5 @@ These templates can be updated with the following commands:
 ./scripts/get_chat_template.py Qwen/Qwen2.5-7B-Instruct                      > models/templates/Qwen-Qwen2.5-7B-Instruct.jinja
 ./scripts/get_chat_template.py Qwen/QwQ-32B                                  > models/templates/Qwen-QwQ-32B.jinja
 ./scripts/get_chat_template.py Qwen/Qwen3-0.6B                               > models/templates/Qwen-Qwen3-0.6B.jinja
-```
+./scripts/get_chat_template.py zai-org/GLM-4.5                               > models/templates/zai-org-GLM-4.5.jinja
+```

requirements/requirements-convert_legacy_llama.txt

@@ -1,5 +1,5 @@
 numpy~=1.26.4
 sentencepiece~=0.2.0
-transformers>=4.45.1,<5.0.0
+transformers>=4.54.1,<5.0.0
 gguf>=0.1.0
 protobuf>=4.21.0,<5.0.0

requirements/requirements-tool_bench.txt

@@ -1,6 +1,6 @@
 aiohttp~=3.9.3
 pytest~=8.3.3
-huggingface_hub~=0.23.2
+huggingface_hub~=0.34.3
 matplotlib~=3.10.0
 numpy~=1.26.4
 openai~=1.55.3

src/llama-arch.cpp

@@ -62,6 +62,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_DEEPSEEK2,        "deepseek2"        },
     { LLM_ARCH_CHATGLM,          "chatglm"          },
     { LLM_ARCH_GLM4,             "glm4"             },
+    { LLM_ARCH_GLM4_MOE,         "glm4moe"          },
     { LLM_ARCH_BITNET,           "bitnet"           },
     { LLM_ARCH_T5,               "t5"               },
     { LLM_ARCH_T5ENCODER,        "t5encoder"        },
@@ -1391,6 +1392,40 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
             { LLM_TENSOR_FFN_POST_NORM,   "blk.%d.post_ffw_norm" },
         },
     },
+    {
+        LLM_ARCH_GLM4_MOE,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,         "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,        "output_norm" },
+            { LLM_TENSOR_OUTPUT,             "output" },
+            { LLM_TENSOR_ATTN_NORM,          "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_POST_NORM,     "blk.%d.post_attention_norm" },
+            { LLM_TENSOR_ATTN_Q,             "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,             "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,             "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,           "blk.%d.attn_output" },
+            { LLM_TENSOR_ATTN_Q_NORM,        "blk.%d.attn_q_norm" },
+            { LLM_TENSOR_ATTN_K_NORM,        "blk.%d.attn_k_norm" },
+            { LLM_TENSOR_FFN_GATE,           "blk.%d.ffn_gate" },        // dense layers
+            { LLM_TENSOR_FFN_DOWN,           "blk.%d.ffn_down" },        // dense layers
+            { LLM_TENSOR_FFN_UP,             "blk.%d.ffn_up" },          // dense layers
+            { LLM_TENSOR_FFN_GATE_INP,       "blk.%d.ffn_gate_inp" },
+            { LLM_TENSOR_FFN_GATE_EXPS,      "blk.%d.ffn_gate_exps" },
+            { LLM_TENSOR_FFN_DOWN_EXPS,      "blk.%d.ffn_down_exps" },
+            { LLM_TENSOR_FFN_UP_EXPS,        "blk.%d.ffn_up_exps" },
+            { LLM_TENSOR_FFN_GATE_SHEXP,     "blk.%d.ffn_gate_shexp" },
+            { LLM_TENSOR_FFN_DOWN_SHEXP,     "blk.%d.ffn_down_shexp" },
+            { LLM_TENSOR_FFN_UP_SHEXP,       "blk.%d.ffn_up_shexp" },
+            { LLM_TENSOR_FFN_EXP_PROBS_B,    "blk.%d.exp_probs_b" },
+            // NextN/MTP tensors - preserved but unused (in final layer, dynamic layer number)
+            { LLM_TENSOR_NEXTN_EH_PROJ,      "blk.%d.eh_proj" },
+            { LLM_TENSOR_NEXTN_EMBED_TOKENS, "blk.%d.embed_tokens" },
+            { LLM_TENSOR_NEXTN_ENORM,        "blk.%d.enorm" },
+            { LLM_TENSOR_NEXTN_HNORM,        "blk.%d.hnorm" },
+            { LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "blk.%d.shared_head.head" },
+            { LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "blk.%d.shared_head.norm" },
+        },
+    },
     {
         LLM_ARCH_BITNET,
         {
@@ -2181,6 +2216,14 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
     {LLM_TENSOR_SHORTCONV_CONV,             {LLM_TENSOR_LAYER_REPEATING, GGML_OP_SSM_CONV}},
     {LLM_TENSOR_SHORTCONV_INPROJ,           {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
     {LLM_TENSOR_SHORTCONV_OUTPROJ,          {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    // NextN/MTP tensors are loaded but never used (reserved for future MTP support)
+    // These tensors only exist in the last layer (layer 46 for GLM-4.5-Air) and are treated as output tensors
+    {LLM_TENSOR_NEXTN_EH_PROJ,              {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_NONE}},
+    {LLM_TENSOR_NEXTN_EMBED_TOKENS,         {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_NONE}},
+    {LLM_TENSOR_NEXTN_ENORM,                {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_NONE}},
+    {LLM_TENSOR_NEXTN_HNORM,                {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_NONE}},
+    {LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD,     {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_NONE}},
+    {LLM_TENSOR_NEXTN_SHARED_HEAD_NORM,     {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_NONE}},
 };
 
 LLM_KV::LLM_KV(llm_arch arch, const char * suffix) : arch(arch), suffix(suffix) {}

src/llama-arch.h

@@ -66,6 +66,7 @@ enum llm_arch {
     LLM_ARCH_DEEPSEEK2,
     LLM_ARCH_CHATGLM,
     LLM_ARCH_GLM4,
+    LLM_ARCH_GLM4_MOE,
     LLM_ARCH_BITNET,
     LLM_ARCH_T5,
     LLM_ARCH_T5ENCODER,
@@ -409,6 +410,12 @@ enum llm_tensor {
     LLM_TENSOR_SHORTCONV_CONV,
     LLM_TENSOR_SHORTCONV_INPROJ,
     LLM_TENSOR_SHORTCONV_OUTPROJ,
+    LLM_TENSOR_NEXTN_EH_PROJ,
+    LLM_TENSOR_NEXTN_EMBED_TOKENS,
+    LLM_TENSOR_NEXTN_ENORM,
+    LLM_TENSOR_NEXTN_HNORM,
+    LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD,
+    LLM_TENSOR_NEXTN_SHARED_HEAD_NORM,
 };
 
 enum llm_tensor_layer {

src/llama-graph.cpp

@@ -749,8 +749,8 @@ ggml_tensor * llm_graph_context::build_ffn(
 
     if (down) {
         cur = build_lora_mm(down, cur);
-        if (arch == LLM_ARCH_GLM4) {
-            // GLM4 seems to have numerical issues with half-precision accumulators
+        if (arch == LLM_ARCH_GLM4 || arch == LLM_ARCH_GLM4_MOE) {
+            // GLM4 and GLM4_MOE seem to have numerical issues with half-precision accumulators
             ggml_mul_mat_set_prec(cur, GGML_PREC_F32);
         }
     }
@@ -1391,8 +1391,8 @@ ggml_tensor * llm_graph_context::build_attn(
 
     if (wo) {
         cur = build_lora_mm(wo, cur);
-        if (arch == LLM_ARCH_GLM4) {
-            // GLM4 seems to have numerical issues with half-precision accumulators
+        if (arch == LLM_ARCH_GLM4 || arch == LLM_ARCH_GLM4_MOE) {
+            // GLM4 and GLM4_MOE seem to have numerical issues with half-precision accumulators
             ggml_mul_mat_set_prec(cur, GGML_PREC_F32);
         }
     }

src/llama-kv-cache-unified.cpp

@@ -39,6 +39,10 @@ llama_kv_cache_unified::llama_kv_cache_unified(
     if (model.arch == LLM_ARCH_GEMMA3N) {
         n_layer_cache = 20;
     }
+    if (model.arch == LLM_ARCH_GLM4_MOE) {
+        // GLM4_MOE: Only process first 46 transformer layers, skip NextN layer
+        n_layer_cache = hparams.n_layer - 1;
+    }
 
     // create a context for each buffer type
     std::map<ggml_backend_buffer_type_t, ggml_context *> ctx_map;