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llm-blender
/
PairRM

Text Generation
Transformers
Safetensors
English
deberta
reward_model
reward-model
RLHF
evaluation
llm
instruction
reranking
Model card Files Files and versions
xet
Community
4

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

  • Libraries
  • Transformers

    How to use llm-blender/PairRM with Transformers:

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

    How to use llm-blender/PairRM with vLLM:

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

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

    How to use llm-blender/PairRM with Docker Model Runner:

    docker model run hf.co/llm-blender/PairRM
New discussion
Resources
  • PR & discussions documentation
  • Code of Conduct
  • Hub documentation

I wonder if there is any possibility that you can make your collected pairwise dataset open-source ?

1
#4 opened over 2 years ago by
fakerbaby

What's the difference between llm-blender/PairRM and llm-blender/pair-ranker?

2
#3 opened over 2 years ago by
nefelibata-mu

Run model with transformers?

2
#2 opened over 2 years ago by
lewtun

please make some examples

🔥 1
#1 opened over 2 years ago by
eramax
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