update: convert-hf-to-gguf.py cleanup for Qwen2MoeForCausalLM

Author: legraphista

convert-hf-to-gguf.py

@@ -1624,54 +1624,13 @@ class Qwen2MoeModel(Model):
     model_arch = gguf.MODEL_ARCH.QWEN2MOE
 
     def set_gguf_parameters(self):
-        self.gguf_writer.add_name(self.dir_model.name)
-        self.gguf_writer.add_block_count(self.block_count)
-
-        if (n_ctx := self.find_hparam(["max_position_embeddings", "n_ctx"], optional=True)) is not None:
-            self.gguf_writer.add_context_length(n_ctx)
-            logger.info(f"gguf: context length = {n_ctx}")
-
-        n_embd = self.find_hparam(["hidden_size", "n_embd"])
-        self.gguf_writer.add_embedding_length(n_embd)
-        logger.info(f"gguf: embedding length = {n_embd}")
-
-        n_head = self.find_hparam(["num_attention_heads", "n_head"])
-        self.gguf_writer.add_head_count(n_head)
-        logger.info(f"gguf: head count = {n_head}")
-
-        if (n_head_kv := self.hparams.get("num_key_value_heads")) is not None:
-            self.gguf_writer.add_head_count_kv(n_head_kv)
-            logger.info(f"gguf: key-value head count = {n_head_kv}")
-
-        if (rope_theta := self.hparams.get("rope_theta")) is not None:
-            self.gguf_writer.add_rope_freq_base(rope_theta)
-            logger.info(f"gguf: rope theta = {rope_theta}")
-        if (f_rms_eps := self.hparams.get("rms_norm_eps")) is not None:
-            self.gguf_writer.add_layer_norm_rms_eps(f_rms_eps)
-            logger.info(f"gguf: rms norm epsilon = {f_rms_eps}")
-        if (f_norm_eps := self.find_hparam(["layer_norm_eps", "layer_norm_epsilon", "norm_epsilon"], optional=True)) is not None:
-            self.gguf_writer.add_layer_norm_eps(f_norm_eps)
-            logger.info(f"gguf: layer norm epsilon = {f_norm_eps}")
-
-        if (n_experts_used := self.hparams.get("num_experts_per_tok")) is not None:
-            self.gguf_writer.add_expert_used_count(n_experts_used)
-            logger.info(f"gguf: experts used count = {n_experts_used}")
-
-        if (n_experts := self.find_hparam(["num_experts", "num_local_experts"])) is not None:
+        super().set_gguf_parameters()
+        if (n_experts := self.hparams.get("num_experts")) is not None:
             self.gguf_writer.add_expert_count(n_experts)
-
         if (moe_intermediate_size := self.hparams.get("moe_intermediate_size")) is not None:
             self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
             logger.info(f"gguf: expert feed forward length = {moe_intermediate_size}")
 
-        if (shared_expert_intermediate_size := self.find_hparam(["shared_expert_intermediate_size","intermediate_size", "n_inner"])) is not None:
-            self.gguf_writer.add_feed_forward_length(shared_expert_intermediate_size)
-            logger.info(f"gguf: feed forward length = {shared_expert_intermediate_size}")
-
-        self.gguf_writer.add_file_type(self.ftype)
-        logger.info(f"gguf: file type = {self.ftype}")
-    
-
     _experts: list[dict[str, Tensor]] | None = None
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: