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

To load a pre-trained model with its language modeling (LM) head using the Hugging Face `transformers` library, you should use the appropriate `AutoModelFor...` class instead of the base `AutoModel`. Here's how to do it: ### 1. **Choose the Correct Auto Class** Select the `AutoModel` variant that matches your model's purpose: - **Causal Language Modeling** (e.g., GPT): `AutoModelForCausalLM` - **Masked Language Modeling** (e.g., BERT): `AutoModelForMaskedLM` - **Sequence-to-Sequence** (e.g., T5, BART): `AutoModelForSeq2SeqLM` ### 2. **Load the Model** Replace `AutoModel` with the correct class for your task: 

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from transformers import AutoModelForCausalLM, AutoTokenizer

# Example: Load GPT-2 with LM head for text generation
model_name = "gpt2"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

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from transformers import AutoModelForMaskedLM

# Example: Load BERT with MLM head for masked prediction
model_name = "bert-base-uncased"
model = AutoModelForMaskedLM.from_pretrained(model_name)
### 3. **Verify the LM Head** The model now includes the LM head. You can test it: 
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input_text = "The capital of France is [MASK]."
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model(**inputs)
### Key Notes: - **Model Compatibility**: Ensure the `model_name` matches the architecture (e.g., don’t use `AutoModelForCausalLM` with BERT). - **Custom Models**: If you’re adding an LM head to a custom model, inherit from the base model and append the head manually. - **Training/Inference**: The LM head is automatically used in `model.generate()` or when computing losses during training. By using the task-specific `AutoModelFor...` class, the LM head is included automatically!

Substring Evaluation

Testing if the previous output contains the string AutoModelForCausalLM: True