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q-future
/
q-align-quality

Text Generation
Transformers
PyTorch
mplug_owl2
Model card Files Files and versions
xet
Community

Instructions to use q-future/q-align-quality with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use q-future/q-align-quality with Transformers:

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

    How to use q-future/q-align-quality with vLLM:

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

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

    How to use q-future/q-align-quality with Docker Model Runner:

    docker model run hf.co/q-future/q-align-quality

arxiv.org/abs/2312.17090

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Collection including q-future/q-align-quality

Visual Scorers!

Collection
Variants of Visual Evaluation Models proposed by [Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-defined Levels]. Use by `model.score()`! • 10 items • Updated Dec 2, 2024 • 3

Paper for q-future/q-align-quality

Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels

Paper • 2312.17090 • Published Dec 28, 2023 • 4
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