Instructions to use zai-org/BPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zai-org/BPO with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="zai-org/BPO")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("zai-org/BPO") model = AutoModelForCausalLM.from_pretrained("zai-org/BPO") - Inference
- Local Apps Settings
- vLLM
How to use zai-org/BPO with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "zai-org/BPO" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "zai-org/BPO", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/zai-org/BPO
- SGLang
How to use zai-org/BPO 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 "zai-org/BPO" \ --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": "zai-org/BPO", "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 "zai-org/BPO" \ --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": "zai-org/BPO", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use zai-org/BPO with Docker Model Runner:
docker model run hf.co/zai-org/BPO
Update README.md
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README.md
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### Data
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Prompt优化模型由隐含人类偏好特征的prompt优化对训练得到,数据集的详细信息在这里。
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The Prompt Optimization Model is trained on prompt optimization pairs which contain human preference features. Detailed information on the dataset can be found [here](https://huggingface.co/datasets/
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### Backbone Model
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The prompt preference optimizer is built on `Llama-2-7b-chat-hf`.
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### Data
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Prompt优化模型由隐含人类偏好特征的prompt优化对训练得到,数据集的详细信息在这里。
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The Prompt Optimization Model is trained on prompt optimization pairs which contain human preference features. Detailed information on the dataset can be found [here](https://huggingface.co/datasets/THUDM/BPO).
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### Backbone Model
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The prompt preference optimizer is built on `Llama-2-7b-chat-hf`.
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