Initial Query

What do I do to fix AutoModel.from_pretrained to make it auto model with lm head

LLM Generation

Query

What do I do to fix AutoModel.from_pretrained to make it auto model with lm head

Output

Okay, the issue is that `AutoModel.from_pretrained` is designed to load the **base** transformer model *without* any specific task head (like a language modeling head, classification head, etc.). It primarily outputs the hidden states from the last layer. To get a model *with* a language modeling (LM) head, you need to use a more specific `AutoModelFor...` class from the Hugging Face `transformers` library. Here's what you need to do, depending on the type of language modeling you want: **1. For Causal Language Modeling (CLM) - Predicting the next token (like GPT-2, Llama, Mistral):** Use `AutoModelForCausalLM`. This class loads the base model *and* adds a head on top specifically designed for predicting the next token in a sequence. 

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   from transformers import AutoTokenizer, AutoModelForCausalLM

   model_name = "gpt2" # Or "meta-llama/Llama-2-7b-hf", "mistralai/Mistral-7B-v0.1", etc.

   # Load the tokenizer
   tokenizer = AutoTokenizer.from_pretrained(model_name)

   # Load the model WITH the Causal LM head
   model = AutoModelForCausalLM.from_pretrained(model_name)

   # Now you can use this model for tasks like text generation
   inputs = tokenizer("Hello, my name is", return_tensors="pt")
   outputs = model.generate(**inputs)
   print(tokenizer.decode(outputs[0], skip_special_tokens=True))
**2. For Masked Language Modeling (MLM) - Predicting masked tokens (like BERT, RoBERTa):** Use `AutoModelForMaskedLM`. This class loads the base model *and* adds a head designed to predict tokens that have been masked in the input. 
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   from transformers import AutoTokenizer, AutoModelForMaskedLM
   import torch

   model_name = "bert-base-uncased" # Or "roberta-base", etc.

   # Load the tokenizer
   tokenizer = AutoTokenizer.from_pretrained(model_name)

   # Load the model WITH the Masked LM head
   model = AutoModelForMaskedLM.from_pretrained(model_name)

   # Example usage
   text = f"Paris is the capital of {tokenizer.mask_token}."
   inputs = tokenizer(text, return_tensors="pt")

   with torch.no_grad():
       outputs = model(**inputs)
       logits = outputs.logits

   # Find the predicted token for the [MASK] position
   mask_token_index = torch.where(inputs["input_ids"] == tokenizer.mask_token_id)[1]
   predicted_token_id = torch.argmax(logits[0, mask_token_index], dim=-1)
   predicted_token = tokenizer.decode(predicted_token_id)

   print(f"Original: {text}")
   print(f"Predicted token for [MASK]: {predicted_token}") # Should be 'france'
**In Summary:** * **Don't use:** `AutoModel.from_pretrained(...)` if you need a task-specific head. * **Use:** * `AutoModelForCausalLM.from_pretrained(...)` for next-token prediction / text generation models. * `AutoModelForMaskedLM.from_pretrained(...)` for masked token prediction models. Choose the class that matches the architecture and intended task of the pre-trained model you are loading. You can usually find this information on the model's page on the Hugging Face Hub.

Substring Evaluation

Testing if the previous output contains the string AutoModelForCausalLM: True