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llm-stacking
/
G_zero_depth

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

Instructions to use llm-stacking/G_zero_depth with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use llm-stacking/G_zero_depth with Transformers:

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

    How to use llm-stacking/G_zero_depth with vLLM:

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

    How to use llm-stacking/G_zero_depth 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 "llm-stacking/G_zero_depth" \
        --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": "llm-stacking/G_zero_depth",
    		"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 "llm-stacking/G_zero_depth" \
            --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": "llm-stacking/G_zero_depth",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use llm-stacking/G_zero_depth with Docker Model Runner:

    docker model run hf.co/llm-stacking/G_zero_depth
G_zero_depth
5.46 GB
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  • 2 contributors
History: 3 commits
Tongxu Luo
Update config.json
5a4d032 verified almost 2 years ago
  • .gitattributes
    1.52 kB
    initial commit almost 2 years ago
  • config.json
    629 Bytes
    Update config.json almost 2 years ago
  • generation_config.json
    132 Bytes
    Upload 8 files almost 2 years ago
  • pytorch_model.bin
    5.46 GB
    xet
    Upload 8 files almost 2 years ago
  • special_tokens_map.json
    414 Bytes
    Upload 8 files almost 2 years ago
  • tokenizer.json
    1.84 MB
    Upload 8 files almost 2 years ago
  • tokenizer.model
    500 kB
    xet
    Upload 8 files almost 2 years ago
  • tokenizer_config.json
    776 Bytes
    Upload 8 files almost 2 years ago
  • vocab.json
    680 kB
    Upload 8 files almost 2 years ago