Instructions to use Xwin-LM/Xwin-LM-7B-V0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Xwin-LM/Xwin-LM-7B-V0.1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Xwin-LM/Xwin-LM-7B-V0.1")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Xwin-LM/Xwin-LM-7B-V0.1") model = AutoModelForCausalLM.from_pretrained("Xwin-LM/Xwin-LM-7B-V0.1") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Xwin-LM/Xwin-LM-7B-V0.1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Xwin-LM/Xwin-LM-7B-V0.1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Xwin-LM/Xwin-LM-7B-V0.1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Xwin-LM/Xwin-LM-7B-V0.1
- SGLang
How to use Xwin-LM/Xwin-LM-7B-V0.1 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 "Xwin-LM/Xwin-LM-7B-V0.1" \ --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": "Xwin-LM/Xwin-LM-7B-V0.1", "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 "Xwin-LM/Xwin-LM-7B-V0.1" \ --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": "Xwin-LM/Xwin-LM-7B-V0.1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Xwin-LM/Xwin-LM-7B-V0.1 with Docker Model Runner:
docker model run hf.co/Xwin-LM/Xwin-LM-7B-V0.1
恭喜突破GPT-4, 这是开源的胜利
恭喜突破GPT-4, 这是开源的胜利, 一个新的起点
如何微调的?进化SFT+强化
就是现在没有比较 权威的评价标准,出来的都说自己很牛
如何微调的?进化SFT+强化
RLHF, 根据title,
Xwin-LM: Powerful, Stable, and Reproducible LLM Alignment
Step up your LLM alignment with Xwin-LM!
Xwin-LM aims to develop and open-source alignment technologies for large language models, including supervised fine-tuning (SFT), reward models (RM), reject sampling, reinforcement learning from human feedback (RLHF), etc. Our first release, built-upon on the Llama2 base models, ranked TOP-1 on AlpacaEval. Notably, it's the first to surpass GPT-4 on this benchmark. The project will be continuously updated.