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xu1998hz
/
InstructScore

Text Generation
Transformers
PyTorch
llama
text-generation-inference
Model card Files Files and versions
xet
Community
2

Instructions to use xu1998hz/InstructScore with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use xu1998hz/InstructScore with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="xu1998hz/InstructScore")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForCausalLM
    
    tokenizer = AutoTokenizer.from_pretrained("xu1998hz/InstructScore")
    model = AutoModelForCausalLM.from_pretrained("xu1998hz/InstructScore", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use xu1998hz/InstructScore with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "xu1998hz/InstructScore"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "xu1998hz/InstructScore",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/xu1998hz/InstructScore
  • SGLang

    How to use xu1998hz/InstructScore 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 "xu1998hz/InstructScore" \
        --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": "xu1998hz/InstructScore",
    		"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 "xu1998hz/InstructScore" \
            --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": "xu1998hz/InstructScore",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use xu1998hz/InstructScore with Docker Model Runner:

    docker model run hf.co/xu1998hz/InstructScore
InstructScore
13.5 GB
Ctrl+K
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  • 2 contributors
History: 8 commits
Wenda Xu
updates all files
338afc4 about 3 years ago
  • figs
    add figures about 3 years ago
  • .gitattributes
    1.48 kB
    initial commit about 3 years ago
  • InstructScore.py
    3.65 kB
    updates all files about 3 years ago
  • README.md
    891 Bytes
    updates all files about 3 years ago
  • added_tokens.json
    21 Bytes
    small fix about 3 years ago
  • config.json
    591 Bytes
    small fix about 3 years ago
  • generation_config.json
    137 Bytes
    small fix about 3 years ago
  • latest
    14 Bytes
    small fix about 3 years ago
  • pytorch_model.bin
    13.5 GB
    xet
    small fix about 3 years ago
  • requirements.txt
    210 Bytes
    updates readme about 3 years ago
  • rng_state_0.pth
    14.6 kB
    xet
    small fix about 3 years ago
  • rng_state_1.pth
    14.6 kB
    xet
    small fix about 3 years ago
  • rng_state_2.pth
    14.6 kB
    xet
    small fix about 3 years ago
  • rng_state_3.pth
    14.6 kB
    xet
    small fix about 3 years ago
  • rng_state_4.pth
    14.6 kB
    xet
    small fix about 3 years ago
  • rng_state_5.pth
    14.6 kB
    xet
    small fix about 3 years ago
  • rng_state_6.pth
    14.6 kB
    xet
    small fix about 3 years ago
  • rng_state_7.pth
    14.6 kB
    xet
    small fix about 3 years ago
  • special_tokens_map.json
    3 Bytes
    small fix about 3 years ago
  • tokenizer.model
    500 kB
    xet
    small fix about 3 years ago
  • tokenizer_config.json
    383 Bytes
    small fix about 3 years ago
  • trainer_state.json
    23.3 kB
    small fix about 3 years ago
  • training_args.bin
    4.92 kB
    xet
    small fix about 3 years ago
  • zero_to_fp32.py
    18.9 kB
    small fix about 3 years ago