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sunshine-lwt
/
TokenPacker-HD-7b-9patch-144token

Text Generation
Transformers
PyTorch
llava
Model card Files Files and versions
xet
Community
1

Instructions to use sunshine-lwt/TokenPacker-HD-7b-9patch-144token with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use sunshine-lwt/TokenPacker-HD-7b-9patch-144token with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="sunshine-lwt/TokenPacker-HD-7b-9patch-144token")
    # Load model directly
    from transformers import AutoProcessor, AutoModelForCausalLM
    
    processor = AutoProcessor.from_pretrained("sunshine-lwt/TokenPacker-HD-7b-9patch-144token")
    model = AutoModelForCausalLM.from_pretrained("sunshine-lwt/TokenPacker-HD-7b-9patch-144token")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use sunshine-lwt/TokenPacker-HD-7b-9patch-144token with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "sunshine-lwt/TokenPacker-HD-7b-9patch-144token"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "sunshine-lwt/TokenPacker-HD-7b-9patch-144token",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/sunshine-lwt/TokenPacker-HD-7b-9patch-144token
  • SGLang

    How to use sunshine-lwt/TokenPacker-HD-7b-9patch-144token 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 "sunshine-lwt/TokenPacker-HD-7b-9patch-144token" \
        --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": "sunshine-lwt/TokenPacker-HD-7b-9patch-144token",
    		"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 "sunshine-lwt/TokenPacker-HD-7b-9patch-144token" \
            --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": "sunshine-lwt/TokenPacker-HD-7b-9patch-144token",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use sunshine-lwt/TokenPacker-HD-7b-9patch-144token with Docker Model Runner:

    docker model run hf.co/sunshine-lwt/TokenPacker-HD-7b-9patch-144token
TokenPacker-HD-7b-9patch-144token
14.2 GB
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  • 1 contributor
History: 3 commits
sunshine-lwt's picture
sunshine-lwt
update
493c9a8 verified almost 2 years ago
  • .gitattributes
    1.52 kB
    initial commit almost 2 years ago
  • README.md
    27 Bytes
    initial commit almost 2 years ago
  • config.json
    1.11 kB
    update almost 2 years ago
  • generation_config.json
    162 Bytes
    upload almost 2 years ago
  • pytorch_model-00001-of-00002.bin
    9.98 GB
    xet
    upload almost 2 years ago
  • pytorch_model-00002-of-00002.bin
    4.18 GB
    xet
    upload almost 2 years ago
  • pytorch_model.bin.index.json
    77.5 kB
    upload almost 2 years ago
  • special_tokens_map.json
    438 Bytes
    upload almost 2 years ago
  • tokenizer.model
    500 kB
    xet
    upload almost 2 years ago
  • tokenizer_config.json
    749 Bytes
    upload almost 2 years ago
  • trainer_state.json
    1.41 MB
    upload almost 2 years ago
  • training_args.bin
    5.5 kB
    xet
    upload almost 2 years ago