Update llama.py
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@ -624,7 +624,6 @@ def LlamaModel_fast_forward(
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else:
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raise TypeError("Unsloth: torch_dtype for models is not bfloat16, float16 or float32!")
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pass
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print(inputs_embeds, inputs_embeds.dtype)
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# Normalized from Gemma
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IS_GEMMA = self.config.model_type.startswith("gemma")
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@ -1011,7 +1010,6 @@ def CausalLM_fast_forward(fast_forward_inference):
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if not RETURN_LOGITS and HAS_CUT_CROSS_ENTROPY and labels is not None:
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print(hidden_states, lm_head)
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n_items = kwargs.get("num_items_in_batch", None) or kwargs.get("n_items", None)
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loss = fused_linear_cross_entropy(
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hidden_states = hidden_states,
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