Instructions to use second-state/Yi-Coder-9B-Chat-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use second-state/Yi-Coder-9B-Chat-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf second-state/Yi-Coder-9B-Chat-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf second-state/Yi-Coder-9B-Chat-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf second-state/Yi-Coder-9B-Chat-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf second-state/Yi-Coder-9B-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 second-state/Yi-Coder-9B-Chat-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf second-state/Yi-Coder-9B-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 second-state/Yi-Coder-9B-Chat-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf second-state/Yi-Coder-9B-Chat-GGUF:Q4_K_M
Use Docker
docker model run hf.co/second-state/Yi-Coder-9B-Chat-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use second-state/Yi-Coder-9B-Chat-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "second-state/Yi-Coder-9B-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": "second-state/Yi-Coder-9B-Chat-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/second-state/Yi-Coder-9B-Chat-GGUF:Q4_K_M
- Ollama
How to use second-state/Yi-Coder-9B-Chat-GGUF with Ollama:
ollama run hf.co/second-state/Yi-Coder-9B-Chat-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use second-state/Yi-Coder-9B-Chat-GGUF with Docker Model Runner:
docker model run hf.co/second-state/Yi-Coder-9B-Chat-GGUF:Q4_K_M
- Lemonade
How to use second-state/Yi-Coder-9B-Chat-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull second-state/Yi-Coder-9B-Chat-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Yi-Coder-9B-Chat-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Download Yi-Coder-9B-Chat-Q3_K_L.gguf from second-state/Yi-Coder-9B-Chat-GGUF: direct link, hf CLI and curl.
- Browser
- Download file 4.69 GB
-
https://huggingface.co/second-state/Yi-Coder-9B-Chat-GGUF/resolve/main/Yi-Coder-9B-Chat-Q3_K_L.gguf
- Command line
-
hf download hf://second-state/Yi-Coder-9B-Chat-GGUF/Yi-Coder-9B-Chat-Q3_K_L.gguf
-
curl -L -o Yi-Coder-9B-Chat-Q3_K_L.gguf https://huggingface.co/second-state/Yi-Coder-9B-Chat-GGUF/resolve/main/Yi-Coder-9B-Chat-Q3_K_L.gguf
4.69 GB
- Xet hash:
- 9155d83ba46ab0be572539ecee4a23112f336341bc7d9ef918557717fcd7e752
- Size of remote file:
- 4.69 GB
- SHA256:
- d7554d8ef4f9913f1873811563db5cb85d4af811b5ca99d523292872c458680e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.