Instructions to use Xwin-LM/Xwin-LM-70B-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-70B-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-70B-V0.1")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Xwin-LM/Xwin-LM-70B-V0.1") model = AutoModelForCausalLM.from_pretrained("Xwin-LM/Xwin-LM-70B-V0.1") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Xwin-LM/Xwin-LM-70B-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-70B-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-70B-V0.1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Xwin-LM/Xwin-LM-70B-V0.1
- SGLang
How to use Xwin-LM/Xwin-LM-70B-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-70B-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-70B-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-70B-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-70B-V0.1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Xwin-LM/Xwin-LM-70B-V0.1 with Docker Model Runner:
docker model run hf.co/Xwin-LM/Xwin-LM-70B-V0.1
It's a bit difficult to deploy the 70B model for verification, so let's keep an eye on how things develop
#4
by wawoshashi - opened
个人部署70B模型来做验证,有点困难, 关注事态发展
Try this quantized version https://huggingface.co/TheBloke/Xwin-LM-70B-V0.1-GGUF which only needs a 48G Vram card, or 40GB RAM cpu only.
You can try it now with llama.cpp
There is also 7B GPTQ Version https://huggingface.co/TheBloke/Xwin-LM-7B-V0.1-GPTQ only need 6G VRAM
I can run 70B quantized GGUF model (Q3_K - Small and offloaded 60/83 layers to GPU ) on 3090 via llama.cpp.