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Electronics
/
Proximity-7B-Task

Text Generation
Transformers
Safetensors
llava_llama
Model card Files Files and versions
xet
Community

Instructions to use Electronics/Proximity-7B-Task with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Electronics/Proximity-7B-Task with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="Electronics/Proximity-7B-Task")
    # Load model directly
    from transformers import AutoModelForCausalLM
    model = AutoModelForCausalLM.from_pretrained("Electronics/Proximity-7B-Task", dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use Electronics/Proximity-7B-Task with vLLM:

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

    How to use Electronics/Proximity-7B-Task 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 "Electronics/Proximity-7B-Task" \
        --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": "Electronics/Proximity-7B-Task",
    		"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 "Electronics/Proximity-7B-Task" \
            --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": "Electronics/Proximity-7B-Task",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use Electronics/Proximity-7B-Task with Docker Model Runner:

    docker model run hf.co/Electronics/Proximity-7B-Task
Proximity-7B-Task
5.42 GB
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  • 1 contributor
History: 2 commits
Electronics's picture
Electronics
commit from root
5f08200 about 2 years ago
  • checkpoint-5000
    commit from root about 2 years ago
  • .gitattributes
    1.52 kB
    initial commit about 2 years ago
  • README.md
    21 Bytes
    initial commit about 2 years ago
  • adapter_config.json
    723 Bytes
    commit from root about 2 years ago
  • adapter_model.safetensors
    640 MB
    xet
    commit from root about 2 years ago
  • config.json
    1.39 kB
    commit from root about 2 years ago
  • non_lora_trainables.bin

    Detected Pickle imports (3)

    • "collections.OrderedDict",
    • "torch.BFloat16Storage",
    • "torch._utils._rebuild_tensor_v2"

    What is a pickle import?

    42 MB
    xet
    commit from root about 2 years ago
  • trainer_state.json
    984 kB
    commit from root about 2 years ago