Update config.json

config.json

@@ -1,70 +1,52 @@
-{
-  "architectures": [
-    "DeepseekV3ForCausalLM"
-  ],
-  "attention_bias": false,
-  "attention_dropout": 0.0,
-  "auto_map": {
-    "AutoConfig": "configuration_deepseek.DeepseekV3Config",
-    "AutoModel": "modeling_deepseek.DeepseekV3Model",
-    "AutoModelForCausalLM": "modeling_deepseek.DeepseekV3ForCausalLM"
-  },
-  "aux_loss_alpha": 0.001,
-  "bos_token_id": 0,
-  "eos_token_id": 1,
-  "ep_size": 1,
-  "first_k_dense_replace": 3,
-  "hidden_act": "silu",
-  "hidden_size": 7168,
-  "initializer_range": 0.02,
-  "intermediate_size": 18432,
-  "kv_lora_rank": 512,
-  "max_position_embeddings": 163840,
-  "model_type": "deepseek_v3",
-  "moe_intermediate_size": 2048,
-  "moe_layer_freq": 1,
-  "n_group": 8,
-  "n_routed_experts": 256,
-  "n_shared_experts": 1,
-  "norm_topk_prob": true,
-  "num_attention_heads": 128,
-  "num_experts_per_tok": 8,
-  "num_hidden_layers": 61,
-  "num_key_value_heads": 128,
-  "num_nextn_predict_layers": 1,
-  "pretraining_tp": 1,
-  "q_lora_rank": 1536,
-  "qk_nope_head_dim": 128,
-  "qk_rope_head_dim": 64,
-  "quantization_config": {
-    "activation_scheme": "dynamic",
-    "fmt": "e4m3",
-    "quant_method": "fp8",
-    "weight_block_size": [
-      128,
-      128
-    ]
-  },
-  "rms_norm_eps": 1e-06,
-  "rope_scaling": {
-    "beta_fast": 32,
-    "beta_slow": 1,
-    "factor": 40,
-    "mscale": 1.0,
-    "mscale_all_dim": 1.0,
-    "original_max_position_embeddings": 4096,
-    "type": "yarn"
-  },
-  "rope_theta": 10000,
-  "routed_scaling_factor": 2.5,
-  "scoring_func": "sigmoid",
-  "seq_aux": true,
-  "tie_word_embeddings": false,
-  "topk_group": 4,
-  "topk_method": "noaux_tc",
-  "torch_dtype": "bfloat16",
-  "transformers_version": "4.46.3",
-  "use_cache": true,
-  "v_head_dim": 128,
-  "vocab_size": 129280
-}
+def load_model_with_quantization_fallback(
+    model_name: str = "deepseek-ai/DeepSeek-R1",
+    trust_remote_code: bool = True,
+    device_map: Optional[Union[str, Dict[str, Any]]] = "auto",
+    **kwargs
+) -> Tuple[PreTrainedModel, PreTrainedTokenizer]:
+
+  try:
+      model = AutoModel.from_pretrained(
+          model_name,
+          trust_remote_code=trust_remote_code,
+          device_map=device_map,
+          **kwargs
+      )
+      tokenizer = AutoTokenizer.from_pretrained(model_name)
+      logger.info("Model loaded successfully with original configuration")
+      return model, tokenizer
+  except ValueError as e:
+      if "Unknown quantization type" in str(e):
+          logger.warning(
+              "Quantization type not supported directly. "
+              "Attempting to load without quantization..."
+          )
+          
+          config = AutoConfig.from_pretrained(
+              model_name,
+              trust_remote_code=trust_remote_code
+          )
+          if hasattr(config, "quantization_config"):
+              delattr(config, "quantization_config")
+          
+          try:
+              model = AutoModel.from_pretrained(
+                  model_name,
+                  config=config,
+                  trust_remote_code=trust_remote_code,
+                  device_map=device_map,
+                  **kwargs
+              )
+              tokenizer = AutoTokenizer.from_pretrained(
+                  model_name,
+                  trust_remote_code=trust_remote_code
+              )
+              logger.info("Model loaded successfully without quantization")
+              return model, tokenizer
+              
+          except Exception as inner_e:
+              logger.error(f"Failed to load model without quantization: {str(inner_e)}")
+              raise
+      else:
+          logger.error(f"Unexpected error during model loading: {str(e)}")
+          raise