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 get an AutoModel with a language modeling head, you should use `AutoModelForCausalLM.from_pretrained()` instead of `AutoModel.from_pretrained()`. Here's how to do it: 

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from transformers import AutoModelForCausalLM

# For causal language modeling (GPT-style models)
model = AutoModelForCausalLM.from_pretrained("model_name")
Or if you're working with masked language modeling (BERT-style models), use: 
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from transformers import AutoModelForMaskedLM

# For masked language modeling (BERT-style models)
model = AutoModelForMaskedLM.from_pretrained("model_name")
The main difference is: - `AutoModel` gives you the base model without any heads - `AutoModelForCausalLM` gives you the model with a language modeling head for causal (left-to-right) language modeling - `AutoModelForMaskedLM` gives you the model with a language modeling head for masked language modeling Choose the appropriate one based on your model type and task. For example, if you're using GPT-2: 
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from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("gpt2")
Or for BERT: 
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from transformers import AutoModelForMaskedLM
model = AutoModelForMaskedLM.from_pretrained("bert-base-uncased")

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

To get an AutoModel with a language modeling head, you should use `AutoModelForCausalLM.from_pretrained()` instead of `AutoModel.from_pretrained()`. Here's how: 

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from transformers import AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained("model_name")
Some other common variants depending on your specific need: - For masked language modeling: 
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from transformers import AutoModelForMaskedLM
model = AutoModelForMaskedLM.from_pretrained("model_name")
- For sequence-to-sequence language modeling: 
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from transformers import AutoModelForSeq2SeqLM
model = AutoModelForSeq2SeqLM.from_pretrained("model_name")
Choose the appropriate class based on your model architecture and task. The most common one for general language models (like GPT models) is `AutoModelForCausalLM`.

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

To get an AutoModel with a language modeling head, you should use `AutoModelForCausalLM.from_pretrained()` instead of `AutoModel.from_pretrained()`. Here's how: 

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from transformers import AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained("model_name")
Different model architectures have different "ForXXX" classes depending on the task: - For causal language modeling (GPT-style): `AutoModelForCausalLM` - For masked language modeling (BERT-style): `AutoModelForMaskedLM` - For sequence-to-sequence (T5-style): `AutoModelForSeq2SeqLM` Choose the appropriate one based on your model type and task. For example: 
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# For GPT-style models
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("gpt2")

# For BERT-style models
from transformers import AutoModelForMaskedLM
model = AutoModelForMaskedLM.from_pretrained("bert-base-uncased")

# For T5-style models
from transformers import AutoModelForSeq2SeqLM
model = AutoModelForSeq2SeqLM.from_pretrained("t5-base")
This will automatically add the appropriate language modeling head to your model.

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

To get an AutoModel with a language modeling head, you should use `AutoModelForCausalLM.from_pretrained()` instead of `AutoModel.from_pretrained()`. Here's how to do it: 

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from transformers import AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained("model_name")
There are also other specific AutoModel classes depending on your task: - For causal language modeling (GPT-style): `AutoModelForCausalLM` - For masked language modeling (BERT-style): `AutoModelForMaskedLM` - For sequence-to-sequence (T5-style): `AutoModelForSeq2SeqLM` - For text generation: `AutoModelForCausalLM` For example: 
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# For GPT-style models
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("gpt2")

# For BERT-style models
from transformers import AutoModelForMaskedLM
model = AutoModelForMaskedLM.from_pretrained("bert-base-uncased")

# For T5-style models
from transformers import AutoModelForSeq2SeqLM
model = AutoModelForSeq2SeqLM.from_pretrained("t5-base")
Choose the appropriate class based on your model architecture and task.

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

To get an AutoModel with a language modeling head, you should use `AutoModelForCausalLM.from_pretrained()` instead of `AutoModel.from_pretrained()`. Here's how: 

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from transformers import AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained("model_name")
Some other common variants depending on your task: - For masked language modeling: 
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from transformers import AutoModelForMaskedLM
model = AutoModelForMaskedLM.from_pretrained("model_name")
- For sequence-to-sequence tasks: 
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from transformers import AutoModelForSeq2SeqLM
model = AutoModelForSeq2SeqLM.from_pretrained("model_name")
The base `AutoModel` class doesn't include the language modeling head, which is why you need to use these specific classes instead. Choose the appropriate one based on your specific use case and model architecture.

Substring Evaluation

Testing if the previous output contains the string AutoModelForCausalLM: True