Instructions to use bartowski/Yi-9B-Coder-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bartowski/Yi-9B-Coder-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="bartowski/Yi-9B-Coder-GGUF")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("bartowski/Yi-9B-Coder-GGUF", dtype="auto") - llama-cpp-python
How to use bartowski/Yi-9B-Coder-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="bartowski/Yi-9B-Coder-GGUF", filename="Yi-9B-Coder-Q2_K.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use bartowski/Yi-9B-Coder-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 bartowski/Yi-9B-Coder-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf bartowski/Yi-9B-Coder-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 bartowski/Yi-9B-Coder-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf bartowski/Yi-9B-Coder-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 bartowski/Yi-9B-Coder-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf bartowski/Yi-9B-Coder-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 bartowski/Yi-9B-Coder-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf bartowski/Yi-9B-Coder-GGUF:Q4_K_M
Use Docker
docker model run hf.co/bartowski/Yi-9B-Coder-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use bartowski/Yi-9B-Coder-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "bartowski/Yi-9B-Coder-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bartowski/Yi-9B-Coder-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/bartowski/Yi-9B-Coder-GGUF:Q4_K_M
- SGLang
How to use bartowski/Yi-9B-Coder-GGUF 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 "bartowski/Yi-9B-Coder-GGUF" \ --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": "bartowski/Yi-9B-Coder-GGUF", "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 "bartowski/Yi-9B-Coder-GGUF" \ --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": "bartowski/Yi-9B-Coder-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Ollama
How to use bartowski/Yi-9B-Coder-GGUF with Ollama:
ollama run hf.co/bartowski/Yi-9B-Coder-GGUF:Q4_K_M
- Unsloth Studio
How to use bartowski/Yi-9B-Coder-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 bartowski/Yi-9B-Coder-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 bartowski/Yi-9B-Coder-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for bartowski/Yi-9B-Coder-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use bartowski/Yi-9B-Coder-GGUF with Docker Model Runner:
docker model run hf.co/bartowski/Yi-9B-Coder-GGUF:Q4_K_M
- Lemonade
How to use bartowski/Yi-9B-Coder-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull bartowski/Yi-9B-Coder-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Yi-9B-Coder-GGUF-Q4_K_M
List all available models
lemonade list
Update README.md with license information
We (01.AI) have updated our license from yi-license to apache-2.0.
Since apache-2.0 enables more free and flexible use and distribution, promoting open collaboration and innovation, we kindly advise that all Yi derivatives currently under the yi-license be updated to the apache-2.0 license.
Thanks for your understanding and cooperation.
Thanks for the update and also thank you so much for switching to apache 2.0, it's such great news for the community and the entire AI scene! Really appreciate everything you all contribute