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` to make it an auto model with an LM head, use the `add_cross_attention` argument and set it to `True`. For example: 

1
auto_model = AutoModel.from_pretrained("bert-base-uncased", add_cross_attention=True)

Substring Evaluation

Testing if the previous output contains the string AutoModelForCausalLM: False

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

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
from transformers import AutoModelForSequenceClassification, AutoTokenizer

# Load the model and tokenizer
model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")

# Prepare the input text
input_text = "I love this movie!"

# Tokenize the input text
inputs = tokenizer(input_text, return_tensors="pt")

# Make a prediction
outputs = model(**inputs)

# Get the predicted class
predicted_class_id = outputs.logits.argmax().item()

# Get the predicted class label
predicted_class_label = model.config.id2label[predicted_class_id]

# Print the predicted class label
print(f"Predicted class: {predicted_class_label}")

Substring Evaluation

Testing if the previous output contains the string AutoModelForCausalLM: False

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` to return an auto model with an LM head, you can use the following code: 

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
from transformers import AutoTokenizer, AutoModelForSequenceClassification

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")

# Use the model
inputs = tokenizer("Hello, world!", return_tensors="pt")
outputs = model(**inputs)

# Get the logits
logits = outputs.logits

# Get the predictions
predictions = torch.argmax(logits, dim=-1)

Substring Evaluation

Testing if the previous output contains the string AutoModelForCausalLM: False

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

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
from transformers import AutoModelWithLMHead, AutoTokenizer

# Load the AutoModelWithLMHead model
model = AutoModelWithLMHead.from_pretrained("model_name")

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

# Prepare text for input
input_ids = tokenizer.encode("Hello, world!", return_tensors="pt")

# Generate a response
output = model.generate(input_ids, max_length=128)

# Decode the output into text
output_text = tokenizer.decode(output[0], skip_special_tokens=True)

Substring Evaluation

Testing if the previous output contains the string AutoModelForCausalLM: False

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 use AutoModel.from_pretrained to load an auto model with a language modeling head, you can specify the model type as "auto" and set the `add_cross_attention` parameter to `True`. Here's an example: 

1
2
3
4
from transformers import AutoModelForSequenceClassification

# Load the auto model with a language modeling head
model = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased", add_cross_attention=True)
This will load the BERT base uncased model with a language modeling head. You can then use the model for sequence classification tasks.

Substring Evaluation

Testing if the previous output contains the string AutoModelForCausalLM: False