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

# Loading Models with a Language Modeling Head To load a model with a language modeling (LM) head using Hugging Face's Transformers library, you need to use the specific `Auto` class that corresponds to the type of language modeling you want to do. The `AutoModel.from_pretrained()` function only loads the base model without any task-specific heads. ## Solution Replace `AutoModel.from_pretrained()` with one of these classes depending on your needs: 

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# For autoregressive/causal language models (GPT, LLaMA, etc.)
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("model_name")

# For masked language models (BERT, RoBERTa, etc.)
from transformers import AutoModelForMaskedLM
model = AutoModelForMaskedLM.from_pretrained("model_name")

# For sequence-to-sequence models (T5, BART, etc.)
from transformers import AutoModelForSeq2SeqLM
model = AutoModelForSeq2SeqLM.from_pretrained("model_name")
## Example 
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from transformers import AutoModelForCausalLM, AutoTokenizer

# Load a GPT-2 model with LM head
model = AutoModelForCausalLM.from_pretrained("gpt2")
tokenizer = AutoTokenizer.from_pretrained("gpt2")

# Now you can use it for text generation
inputs = tokenizer("Hello, I'm a language model", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Choose the appropriate class based on the architecture of your model and the task you want to perform.

Substring Evaluation

Testing if the previous output contains the string AutoModelForCausalLM: True

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

# How to Use AutoModelForCausalLM Instead of AutoModel If you're trying to load a model with a language modeling head using Hugging Face's Transformers library, you should use the specific Auto classes designed for this purpose rather than the base `AutoModel`. ## Solution: Replace: 

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from transformers import AutoModel
model = AutoModel.from_pretrained("your-model-name")
With one of these, depending on your use case: 
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# For causal language models (GPT, OPT, Llama, etc.)
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("your-model-name")

# For masked language models (BERT, RoBERTa, etc.)
from transformers import AutoModelForMaskedLM
model = AutoModelForMaskedLM.from_pretrained("your-model-name")

# For sequence-to-sequence models (T5, BART, etc.)
from transformers import AutoModelForSeq2SeqLM
model = AutoModelForSeq2SeqLM.from_pretrained("your-model-name")
`AutoModel` only loads the base transformer model without any task-specific heads, which is why you're not getting the language modeling functionality you need. Is there a specific model architecture you're working with? I can provide more tailored advice if you share more details.

Substring Evaluation

Testing if the previous output contains the string AutoModelForCausalLM: True