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princeton-nlp
/
Sheared-LLaMA-1.3B

Text Generation
Transformers
PyTorch
llama
text-generation-inference
Model card Files Files and versions
xet
Community
14

Instructions to use princeton-nlp/Sheared-LLaMA-1.3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use princeton-nlp/Sheared-LLaMA-1.3B with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="princeton-nlp/Sheared-LLaMA-1.3B")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForCausalLM
    
    tokenizer = AutoTokenizer.from_pretrained("princeton-nlp/Sheared-LLaMA-1.3B")
    model = AutoModelForCausalLM.from_pretrained("princeton-nlp/Sheared-LLaMA-1.3B")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use princeton-nlp/Sheared-LLaMA-1.3B with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "princeton-nlp/Sheared-LLaMA-1.3B"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "princeton-nlp/Sheared-LLaMA-1.3B",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/princeton-nlp/Sheared-LLaMA-1.3B
  • SGLang

    How to use princeton-nlp/Sheared-LLaMA-1.3B 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 "princeton-nlp/Sheared-LLaMA-1.3B" \
        --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": "princeton-nlp/Sheared-LLaMA-1.3B",
    		"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 "princeton-nlp/Sheared-LLaMA-1.3B" \
            --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": "princeton-nlp/Sheared-LLaMA-1.3B",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use princeton-nlp/Sheared-LLaMA-1.3B with Docker Model Runner:

    docker model run hf.co/princeton-nlp/Sheared-LLaMA-1.3B
New discussion
Resources
  • PR & discussions documentation
  • Code of Conduct
  • Hub documentation

include sample code to run the model in readme

#14 opened almost 2 years ago by
oddlyspaced

Can provide a use sample?

1
#13 opened almost 2 years ago by
wenine

Adding `safetensors` variant of this model

1
#12 opened about 2 years ago by
SFconvertbot

Adding `safetensors` variant of this model

#11 opened over 2 years ago by
SFconvertbot

Loss without grad_fn when using transformers Trainer suite

1
#10 opened over 2 years ago by
syboomsysy

Adding `safetensors` variant of this model

👍 1
#9 opened over 2 years ago by
SFconvertbot

Adding `safetensors` variant of this model

#8 opened over 2 years ago by
SFconvertbot

Adding `safetensors` variant of this model

#7 opened over 2 years ago by
SFconvertbot

Instruct version

2
#5 opened over 2 years ago by
kimihailv

Great work + Excellent model

❤️ 2
1
#3 opened over 2 years ago by
doberst

Congratulations on this!!!!!

❤️👍 8
#2 opened over 2 years ago by
appvoid
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