Instructions to use Qwen/CodeQwen1.5-7B-Chat-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use Qwen/CodeQwen1.5-7B-Chat-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Qwen/CodeQwen1.5-7B-Chat-GGUF", filename="codeqwen-1_5-7b-chat-q2_k.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use Qwen/CodeQwen1.5-7B-Chat-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Qwen/CodeQwen1.5-7B-Chat-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf Qwen/CodeQwen1.5-7B-Chat-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Qwen/CodeQwen1.5-7B-Chat-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf Qwen/CodeQwen1.5-7B-Chat-GGUF:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf Qwen/CodeQwen1.5-7B-Chat-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Qwen/CodeQwen1.5-7B-Chat-GGUF:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf Qwen/CodeQwen1.5-7B-Chat-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Qwen/CodeQwen1.5-7B-Chat-GGUF:Q4_K_M
Use Docker
docker model run hf.co/Qwen/CodeQwen1.5-7B-Chat-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use Qwen/CodeQwen1.5-7B-Chat-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Qwen/CodeQwen1.5-7B-Chat-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Qwen/CodeQwen1.5-7B-Chat-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Qwen/CodeQwen1.5-7B-Chat-GGUF:Q4_K_M
- Ollama
How to use Qwen/CodeQwen1.5-7B-Chat-GGUF with Ollama:
ollama run hf.co/Qwen/CodeQwen1.5-7B-Chat-GGUF:Q4_K_M
- Unsloth Studio new
How to use Qwen/CodeQwen1.5-7B-Chat-GGUF 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 Qwen/CodeQwen1.5-7B-Chat-GGUF 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 Qwen/CodeQwen1.5-7B-Chat-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Qwen/CodeQwen1.5-7B-Chat-GGUF to start chatting
- Docker Model Runner
How to use Qwen/CodeQwen1.5-7B-Chat-GGUF with Docker Model Runner:
docker model run hf.co/Qwen/CodeQwen1.5-7B-Chat-GGUF:Q4_K_M
- Lemonade
How to use Qwen/CodeQwen1.5-7B-Chat-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Qwen/CodeQwen1.5-7B-Chat-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.CodeQwen1.5-7B-Chat-GGUF-Q4_K_M
List all available models
lemonade list
CodeQwen1.5-7B-Chat-GGUF
Introduction
CodeQwen1.5 is the Code-Specific version of Qwen1.5. It is a transformer-based decoder-only language model pretrained on a large amount of data of codes.
- Strong code generation capabilities and competitve performance across a series of benchmarks;
- Supporting long context understanding and generation with the context length of 64K tokens;
- Supporting 92 coding languages
- Excellent performance in text-to-SQL, bug fix, etc.
For more details, please refer to our blog post and GitHub repo.
In this repo, we provide quantized models in the GGUF formats, including q2_k, q3_k_m, q4_0, q4_k_m, q5_0, q5_k_m, q6_k and q8_0.
Model Details
CodeQwen1.5 is based on Qwen1.5, a language model series including decoder language models of different model sizes. It is trained on 3 trillion tokens of data of codes, and it includes group query attention (GQA) for efficient inference.
Requirements
We advise you to clone llama.cpp and install it following the official guide.
How to use
Cloning the repo may be inefficient, and thus you can manually download the GGUF file that you need or use huggingface-cli (pip install huggingface_hub) as shown below:
huggingface-cli download Qwen/CodeQwen1.5-7B-Chat-GGUF codeqwen-1_5-7b-chat-q5_k_m.gguf --local-dir . --local-dir-use-symlinks False
We demonstrate how to use llama.cpp to run Qwen1.5:
./main -m codeqwen-1_5-7b-chat-q5_k_m.gguf -n 512 --color -i -cml -f prompts/chat-with-qwen.txt
Citation
If you find our work helpful, feel free to give us a cite.
@article{qwen,
title={Qwen Technical Report},
author={Jinze Bai and Shuai Bai and Yunfei Chu and Zeyu Cui and Kai Dang and Xiaodong Deng and Yang Fan and Wenbin Ge and Yu Han and Fei Huang and Binyuan Hui and Luo Ji and Mei Li and Junyang Lin and Runji Lin and Dayiheng Liu and Gao Liu and Chengqiang Lu and Keming Lu and Jianxin Ma and Rui Men and Xingzhang Ren and Xuancheng Ren and Chuanqi Tan and Sinan Tan and Jianhong Tu and Peng Wang and Shijie Wang and Wei Wang and Shengguang Wu and Benfeng Xu and Jin Xu and An Yang and Hao Yang and Jian Yang and Shusheng Yang and Yang Yao and Bowen Yu and Hongyi Yuan and Zheng Yuan and Jianwei Zhang and Xingxuan Zhang and Yichang Zhang and Zhenru Zhang and Chang Zhou and Jingren Zhou and Xiaohuan Zhou and Tianhang Zhu},
journal={arXiv preprint arXiv:2309.16609},
year={2023}
}
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