Instructions to use openchat/openchat_v2_w with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openchat/openchat_v2_w with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="openchat/openchat_v2_w")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("openchat/openchat_v2_w") model = AutoModelForCausalLM.from_pretrained("openchat/openchat_v2_w") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use openchat/openchat_v2_w with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "openchat/openchat_v2_w" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "openchat/openchat_v2_w", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/openchat/openchat_v2_w
- SGLang
How to use openchat/openchat_v2_w 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 "openchat/openchat_v2_w" \ --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": "openchat/openchat_v2_w", "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 "openchat/openchat_v2_w" \ --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": "openchat/openchat_v2_w", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use openchat/openchat_v2_w with Docker Model Runner:
docker model run hf.co/openchat/openchat_v2_w
Hello, can you elaborate on these conditional behavior cloning and weighted behavior cloning?
What are they? I looked the terms up on Google and found nothing.
If it's RLHF, what differentiates the two methods? Thanks
Thanks for your interest. In short, we simply use different prompts like "Assistant GPT3.5" and "Assistant GPT4". We are preparing a paper to elaborate on our technical report.
Thanks for your interest. In short, we simply use different prompts like "Assistant GPT3.5" and "Assistant GPT4". We are preparing a paper to elaborate on our technical report.
Will it be a significant performance drop if not using conditional behavior cloning, i.e., all 80K samples with a uniform "Assistant:" prompt?
Yes. This may have the same performance as Vicuna.