Text Generation
Transformers
Safetensors
English
deberta
reward_model
reward-model
RLHF
evaluation
llm
instruction
reranking
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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- 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
Dongfu Jiang commited on
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README.md
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datasets:
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- openai/summarize_from_feedback
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- openai/webgpt_comparisons
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- Dahoas/
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- Anthropic/hh-rlhf
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- lmsys/chatbot_arena_conversations
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- openbmb/UltraFeedback
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### Training Datasets
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- [openai/summarize_from_feedback](https://huggingface.co/datasets/openai/summarize_from_feedback)
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- [openai/webgpt_comparisons](https://huggingface.co/datasets/openai/webgpt_comparisons)
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- [Dahoas/
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- [Anthropic/hh-rlhf](https://huggingface.co/datasets/Anthropic/hh-rlhf)
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- [lmsys/chatbot_arena_conversations](https://huggingface.co/datasets/lmsys/chatbot_arena_conversations)
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- [openbmb/UltraFeedback](https://huggingface.co/datasets/openbmb/UltraFeedback)
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datasets:
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- openai/summarize_from_feedback
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- openai/webgpt_comparisons
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- Dahoas/synthetic-instruct-gptj-pairwise
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- Anthropic/hh-rlhf
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- lmsys/chatbot_arena_conversations
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- openbmb/UltraFeedback
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### Training Datasets
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- [openai/summarize_from_feedback](https://huggingface.co/datasets/openai/summarize_from_feedback)
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- [openai/webgpt_comparisons](https://huggingface.co/datasets/openai/webgpt_comparisons)
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- [Dahoas/synthetic-instruct-gptj-pairwise](https://huggingface.co/datasets/Dahoas/synthetic-instruct-gptj-pairwise)
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- [Anthropic/hh-rlhf](https://huggingface.co/datasets/Anthropic/hh-rlhf)
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- [lmsys/chatbot_arena_conversations](https://huggingface.co/datasets/lmsys/chatbot_arena_conversations)
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- [openbmb/UltraFeedback](https://huggingface.co/datasets/openbmb/UltraFeedback)
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