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 fix `AutoModel.from_pretrained` and make it an auto model with an LM head, you can use the `AutoModelForCausalLM` or `AutoModelForMaskedLM` class from the Hugging Face Transformers library, depending on your specific use case. Here are some possible solutions: **1. Use `AutoModelForCausalLM` for causal language modeling:** 

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from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "your_model_name"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
This will load a pre-trained model with a causal language modeling head. **2. Use `AutoModelForMaskedLM` for masked language modeling:** 
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from transformers import AutoModelForMaskedLM, AutoTokenizer

model_name = "your_model_name"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForMaskedLM.from_pretrained(model_name)
This will load a pre-trained model with a masked language modeling head. **3. Add a language modeling head to a pre-trained model:** 
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from transformers import AutoModel, AutoTokenizer
from transformers import BertLMHead

model_name = "your_model_name"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

# Add a language modeling head
lm_head = BertLMHead(model.config)
model = BertForMaskedLM(model.config, model)
This will add a language modeling head to a pre-trained model. Note that the specific class and configuration may vary depending on the model architecture and your specific use case. You can refer to the Hugging Face Transformers documentation for more information on the available models and classes.

Substring Evaluation

Testing if the previous output contains the string AutoModelForCausalLM: True