Instructions to use meta-llama/Llama-3.2-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use meta-llama/Llama-3.2-1B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="meta-llama/Llama-3.2-1B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.2-1B")
model = AutoModelForMultimodalLM.from_pretrained("meta-llama/Llama-3.2-1B")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use meta-llama/Llama-3.2-1B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "meta-llama/Llama-3.2-1B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "meta-llama/Llama-3.2-1B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/meta-llama/Llama-3.2-1B

SGLang

How to use meta-llama/Llama-3.2-1B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "meta-llama/Llama-3.2-1B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "meta-llama/Llama-3.2-1B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "meta-llama/Llama-3.2-1B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "meta-llama/Llama-3.2-1B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use meta-llama/Llama-3.2-1B with Docker Model Runner:
```
docker model run hf.co/meta-llama/Llama-3.2-1B
```

Error when evaluate this model using pipeline

#13

by xiaxin1998 - opened Sep 28, 2024

Discussion

xiaxin1998

Sep 28, 2024

Hello, I finetuned the model and then when I want to evaluate it, I need to get the scores as well. I set output_scores=True, return_dict_in_generate=True but I got an error :
predictions_with_scores = pipe(input_texts, max_new_tokens=30, num_beams=k, num_return_sequences=1, output_scores=True, return_dict_in_generate=True)
File "/opt/anaconda3/envs/openllama/lib/python3.10/site-packages/transformers/pipelines/text_generation.py", line 272, in call
return super().call(text_inputs, **kwargs)
File "/opt/anaconda3/envs/openllama/lib/python3.10/site-packages/transformers/pipelines/base.py", line 1249, in call
outputs = list(final_iterator)
File "/opt/anaconda3/envs/openllama/lib/python3.10/site-packages/transformers/pipelines/pt_utils.py", line 124, in next
item = next(self.iterator)
File "/opt/anaconda3/envs/openllama/lib/python3.10/site-packages/transformers/pipelines/pt_utils.py", line 125, in next
processed = self.infer(item, **self.params)
File "/opt/anaconda3/envs/openllama/lib/python3.10/site-packages/transformers/pipelines/base.py", line 1175, in forward
model_outputs = self._forward(model_inputs, **forward_params)
File "/opt/anaconda3/envs/openllama/lib/python3.10/site-packages/transformers/pipelines/text_generation.py", line 371, in _forward
out_b = generated_sequence.shape[0]
AttributeError: 'GenerateBeamDecoderOnlyOutput' object has no attribute 'shape'

My codes are:
for batch in tqdm(dataloader):
# Decode input IDs into text for the pipeline
input_texts = pipe.tokenizer.batch_decode(batch["input_ids"], skip_special_tokens=True)
predictions_with_scores = pipe(input_texts, max_new_tokens=30, num_beams=k, num_return_sequences=1, output_scores=True, return_dict_in_generate=True)

    # The generated sentences are the predictions
    generated_sents = [pred for pred in predictions_with_scores['sequences']]

    # Get the scores for each prediction
    prediction_scores = predictions_with_scores['sequences_scores']  # These are the scores returned from pipeline

    # Decode the gold (ground truth) sentences
    gold_sents = pipe.tokenizer.batch_decode(batch['label'], skip_special_tokens=True)

my transformers version : 4.45.1

Can anyone tell me how to get the score? Or why it fails when using 'output_scores=True, return_dict_in_generate=True'?

Thanks.

xiaxin1998

Sep 29, 2024

The problem seems to be the internal implementation of pipeline. Currently, when using beam search, you will get this error. The solution now is using the old version 'model.generate()' instead of pipeline.

ernestyalumni

Oct 3, 2024

I also ended up using an instance of AutoModelForCausalLM (which ends up yielding an instance of LlamaModelForCausal) as well instead of pipeline. Looking at the AttributeError, note that GenerateBeamDecoderOnlyoutput is defined in modeling_outputs.py of transformers library, which uses a base class of ModelOutput. Which in turn inherits from OrderedDict. So a .shape attribute isn't expected.

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