fsdp fft loading on meta device

docs/input_output.qmd

@@ -205,7 +205,7 @@ ds = load_from_disk(f'last_run_prepared/{directory[0]}/')
     hi there!.  goodbye  farewell</s>
 ```
 
-We can check that the right tokens are ignored by comparing the labels
+We can check that the right tokens are ingored by comparing the labels
 to each token:
 
 ```python

src/axolotl/utils/config/models/input/v0_4_1/__init__.py

@@ -321,8 +321,6 @@ class ModelInputConfig(BaseModel):
     )
     trust_remote_code: Optional[bool] = None
 
-    model_kwargs: Optional[Dict[str, Any]] = None
-
     @field_validator("trust_remote_code")
     @classmethod
     def hint_trust_remote_code(cls, trust_remote_code):
@@ -616,8 +614,6 @@ class AxolotlInputConfig(
     flash_attn_fuse_mlp: Optional[bool] = None
     flash_optimum: Optional[bool] = None
 
-    eager_attention: Optional[bool] = None
-
     unsloth_cross_entropy_loss: Optional[bool] = None
     unsloth_lora_mlp: Optional[bool] = None
     unsloth_lora_qkv: Optional[bool] = None

src/axolotl/utils/models.py

@@ -655,11 +655,19 @@ def load_model(
                 if "device_map" in model_kwargs:
                     del model_kwargs["device_map"]
 
-            model = AutoModelForCausalLM.from_pretrained(
-                base_model,
-                config=model_config,
-                **model_kwargs,
-            )
+            if cfg.fsdp and not cfg.adapter and cfg.local_rank != 0:
+                with init_empty_weights():
+                    model = AutoModelForCausalLM.from_pretrained(
+                        base_model,
+                        config=model_config,
+                        **model_kwargs,
+                    )
+            else:
+                model = AutoModelForCausalLM.from_pretrained(
+                    base_model,
+                    config=model_config,
+                    **model_kwargs,
+                )
 
             if cfg.flash_attention and not inference:
                 from axolotl.monkeypatch.llama_attn_hijack_flash import (