Added support of MTP speculative decoding on zai/glm-4.7-flash

conversion/glm.py

@@ -203,11 +203,26 @@ def prepare_tensors(self):
 
 @ModelBase.register("Glm4MoeLiteForCausalLM")
 class Glm4MoeLiteModel(DeepseekV2Model):
-    model_arch = gguf.MODEL_ARCH.DEEPSEEK2
+    model_arch = gguf.MODEL_ARCH.GLM4_MOE_LITE
+    skip_mtp = False
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        if type(self).no_mtp:
+            type(self).skip_mtp = True
+        self.block_count = self.hparams["num_hidden_layers"] + (0 if type(self).skip_mtp else self.hparams.get("num_nextn_predict_layers", 0))
+        self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
 
     def set_vocab(self):
         return self._set_vocab_glm()
 
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+
+        if not type(self).skip_mtp:
+            if (num_nextn_predict_layers := self.hparams.get("num_nextn_predict_layers")) is not None:
+                self.gguf_writer.add_nextn_predict_layers(num_nextn_predict_layers)
+
 
 @ModelBase.register("GlmMoeDsaForCausalLM")
 class GlmMoeDsaModel(DeepseekV2Model):

convert_hf_to_gguf.py

@@ -261,8 +261,9 @@ def main() -> None:
         if args.mtp or args.no_mtp:
             from conversion.qwen import _Qwen35MtpMixin
             from conversion.step3 import Step35Model
-            if not (issubclass(model_class, _Qwen35MtpMixin) or issubclass(model_class, Step35Model)):
-                logger.error("--mtp / --no-mtp are only supported for Qwen3.5/3.6 and Step3.5 text variants today")
+            from conversion.glm import Glm4MoeLiteModel
+            if not (issubclass(model_class, _Qwen35MtpMixin) or issubclass(model_class, Step35Model) or issubclass(model_class, Glm4MoeLiteModel)):
+                logger.error("--mtp / --no-mtp are only supported for Qwen3.5/3.6, Step3.5 and GLM-4.7 text variants today")
                 sys.exit(1)
             if args.no_mtp:
                 model_class.no_mtp = True

gguf-py/gguf/constants.py

@@ -484,6 +484,7 @@ class MODEL_ARCH(IntEnum):
     CHATGLM          = auto()
     GLM4             = auto()
     GLM4_MOE         = auto()
+    GLM4_MOE_LITE    = auto()
     GLM_DSA          = auto()
     BITNET           = auto()
     T5               = auto()
@@ -1064,6 +1065,7 @@ class MODEL_TENSOR(IntEnum):
     MODEL_ARCH.CHATGLM:          "chatglm",
     MODEL_ARCH.GLM4:             "glm4",
     MODEL_ARCH.GLM4_MOE:         "glm4moe",
+    MODEL_ARCH.GLM4_MOE_LITE:    "glm4-moe-lite",
     MODEL_ARCH.GLM_DSA:          "glm-dsa",
     MODEL_ARCH.BITNET:           "bitnet",
     MODEL_ARCH.T5:               "t5",
@@ -3343,6 +3345,42 @@ class MODEL_TENSOR(IntEnum):
         MODEL_TENSOR.NEXTN_SHARED_HEAD_HEAD,
         MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM,
     ],
+    MODEL_ARCH.GLM4_MOE_LITE: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ROPE_FREQS,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_Q_A,
+        MODEL_TENSOR.ATTN_Q_B,
+        MODEL_TENSOR.ATTN_KV_A_MQA,
+        MODEL_TENSOR.ATTN_KV_B,
+        MODEL_TENSOR.ATTN_K_B,
+        MODEL_TENSOR.ATTN_V_B,
+        MODEL_TENSOR.ATTN_Q_A_NORM,
+        MODEL_TENSOR.ATTN_KV_A_NORM,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.ATTN_ROT_EMBD,
+        MODEL_TENSOR.FFN_GATE_INP,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+        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,
+        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.GLM_DSA: [
         MODEL_TENSOR.TOKEN_EMBD,
         MODEL_TENSOR.OUTPUT_NORM,
@@ -4453,6 +4491,10 @@ class MODEL_TENSOR(IntEnum):
         MODEL_TENSOR.ROPE_FREQS,
         MODEL_TENSOR.ATTN_ROT_EMBD,
     ],
+    MODEL_ARCH.GLM4_MOE_LITE: [
+        MODEL_TENSOR.ROPE_FREQS,
+        MODEL_TENSOR.ATTN_ROT_EMBD,
+    ],
     MODEL_ARCH.CHATGLM: [
         MODEL_TENSOR.ROPE_FREQS,
     ],

src/llama-arch.cpp

@@ -81,6 +81,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_CHATGLM,          "chatglm"          },
     { LLM_ARCH_GLM4,             "glm4"             },
     { LLM_ARCH_GLM4_MOE,         "glm4moe"          },
+    { LLM_ARCH_GLM4_MOE_LITE,    "glm4-moe-lite"    },
     { LLM_ARCH_GLM_DSA,          "glm-dsa"          },
     { LLM_ARCH_BITNET,           "bitnet"           },
     { LLM_ARCH_T5,               "t5"               },

src/llama-arch.h

@@ -86,6 +86,7 @@ enum llm_arch {
     LLM_ARCH_CHATGLM,
     LLM_ARCH_GLM4,
     LLM_ARCH_GLM4_MOE,
+    LLM_ARCH_GLM4_MOE_LITE,
     LLM_ARCH_GLM_DSA,
     LLM_ARCH_BITNET,
     LLM_ARCH_T5,

src/llama-model.cpp

@@ -194,6 +194,8 @@ static llama_model * llama_model_mapping(llm_arch arch, const llama_model_params
             return new llama_model_glm4(params);
         case LLM_ARCH_GLM4_MOE:
             return new llama_model_glm4_moe(params);
+        case LLM_ARCH_GLM4_MOE_LITE:
+            return new llama_model_glm4_moe_lite(params);
         case LLM_ARCH_BITNET:
             return new llama_model_bitnet(params);
         case LLM_ARCH_T5:
@@ -1855,7 +1857,7 @@ void llama_model::print_info() const {
             LLAMA_LOG_INFO("%s: expert_weights_scale  = %.1f\n",   __func__, hparams.expert_weights_scale);
         }
 
-        if (arch == LLM_ARCH_DEEPSEEK2 || arch == LLM_ARCH_DEEPSEEK2OCR || arch == LLM_ARCH_DEEPSEEK32 || arch == LLM_ARCH_GLM_DSA || arch == LLM_ARCH_MISTRAL4) {
+        if (arch == LLM_ARCH_DEEPSEEK2 || arch == LLM_ARCH_DEEPSEEK2OCR || arch == LLM_ARCH_DEEPSEEK32 || arch == LLM_ARCH_GLM_DSA || arch == LLM_ARCH_GLM4_MOE_LITE || arch == LLM_ARCH_MISTRAL4) {
             LLAMA_LOG_INFO("%s: n_layer_dense_lead    = %d\n",     __func__, hparams.n_layer_dense_lead);
             LLAMA_LOG_INFO("%s: n_lora_q              = %d\n",     __func__, hparams.n_lora_q);
             LLAMA_LOG_INFO("%s: n_lora_kv             = %d\n",     __func__, hparams.n_lora_kv);
@@ -2178,6 +2180,14 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
                         }
                     }
 
+                    if ((arch == LLM_ARCH_GLM4_MOE_LITE) && hparams.n_layer_nextn > 0) {
+                        if (params.ctx_type == LLAMA_CONTEXT_TYPE_MTP) {
+                            filter = [&](uint32_t il) { return il >= hparams.n_layer(); };
+                        } else {
+                            filter = [&](uint32_t il) { return il <  hparams.n_layer(); };
+                        }
+                    }
+
                     if (arch == LLM_ARCH_DEEPSEEK4) {
                         GGML_ASSERT(hparams.swa_type != LLAMA_SWA_TYPE_NONE);
 
@@ -2472,6 +2482,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
         case LLM_ARCH_LLAMA_EMBED:
         case LLM_ARCH_MAINCODER:
         case LLM_ARCH_GLM_DSA:
+        case LLM_ARCH_GLM4_MOE_LITE:
             return LLAMA_ROPE_TYPE_NORM;
 
         // the pairs of head values are offset by n_rot/2

src/models/deepseek2.cpp

@@ -423,6 +423,9 @@ llama_model_deepseek2::graph::graph(const llama_model & model, const llm_graph_p
     }
     cur = inpL;
 
+    cb(cur, "h_nextn", -1);
+    res->t_h_nextn = cur;
+
     cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
 
     cb(cur, "result_norm", -1);