Instructions to use Qwen/Qwen-7B-Chat-Int4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Qwen/Qwen-7B-Chat-Int4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Qwen/Qwen-7B-Chat-Int4", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-7B-Chat-Int4", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Qwen/Qwen-7B-Chat-Int4 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Qwen/Qwen-7B-Chat-Int4" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Qwen/Qwen-7B-Chat-Int4", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Qwen/Qwen-7B-Chat-Int4
- SGLang
How to use Qwen/Qwen-7B-Chat-Int4 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 "Qwen/Qwen-7B-Chat-Int4" \ --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": "Qwen/Qwen-7B-Chat-Int4", "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 "Qwen/Qwen-7B-Chat-Int4" \ --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": "Qwen/Qwen-7B-Chat-Int4", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Qwen/Qwen-7B-Chat-Int4 with Docker Model Runner:
docker model run hf.co/Qwen/Qwen-7B-Chat-Int4
Add ApplyRoPE and RMSNorm kernels written in OpenAI Triton
#10
by Cheshire94 - opened
No description provided.
Cheshire94 changed pull request title from pr/9 to pr/10
This PR add kernels of ApplyRoPE and RMSNorm written in OpenAI Triton. These kernels offer better performance, support a wider range of GPU architectures (including V100 and T4), and require no pre-compilation, compared with flash-attn. They are enabled automatically if Triton is installed (usually bundled with PyTorch 2.x).
Cheshire94 changed pull request title from pr/10 to Add ApplyRoPE and RMSNorm kernels written in OpenAI Triton
Cheshire94 changed pull request status to closed