Instructions to use minhvn4/VLLM-QWEN-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Local Apps
- Unsloth Studio new
How to use minhvn4/VLLM-QWEN-4bit with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for minhvn4/VLLM-QWEN-4bit to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for minhvn4/VLLM-QWEN-4bit to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for minhvn4/VLLM-QWEN-4bit to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="minhvn4/VLLM-QWEN-4bit", max_seq_length=2048, )
- Xet hash:
- 18f5ee8c52e9d225044dd7ef664f390226accb3a4e27ac5d3e5a71e8a4eebf2a
- Size of remote file:
- 11.4 MB
- SHA256:
- fab42efe8d17406525a9154b728cf9e957629a8ed7ce997770efdd71128c6a1a
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