Instructions to use shsgrnd/SSAFY_gitcat-local-llm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use shsgrnd/SSAFY_gitcat-local-llm with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="shsgrnd/SSAFY_gitcat-local-llm", filename="gitcat-v3-dpo-merged-Q4_K_M.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 shsgrnd/SSAFY_gitcat-local-llm with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf shsgrnd/SSAFY_gitcat-local-llm:Q4_K_M # Run inference directly in the terminal: llama-cli -hf shsgrnd/SSAFY_gitcat-local-llm:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf shsgrnd/SSAFY_gitcat-local-llm:Q4_K_M # Run inference directly in the terminal: llama-cli -hf shsgrnd/SSAFY_gitcat-local-llm: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 shsgrnd/SSAFY_gitcat-local-llm:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf shsgrnd/SSAFY_gitcat-local-llm: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 shsgrnd/SSAFY_gitcat-local-llm:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf shsgrnd/SSAFY_gitcat-local-llm:Q4_K_M
Use Docker
docker model run hf.co/shsgrnd/SSAFY_gitcat-local-llm:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use shsgrnd/SSAFY_gitcat-local-llm with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "shsgrnd/SSAFY_gitcat-local-llm" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "shsgrnd/SSAFY_gitcat-local-llm", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/shsgrnd/SSAFY_gitcat-local-llm:Q4_K_M
- Ollama
How to use shsgrnd/SSAFY_gitcat-local-llm with Ollama:
ollama run hf.co/shsgrnd/SSAFY_gitcat-local-llm:Q4_K_M
- Unsloth Studio new
How to use shsgrnd/SSAFY_gitcat-local-llm 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 shsgrnd/SSAFY_gitcat-local-llm 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 shsgrnd/SSAFY_gitcat-local-llm to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for shsgrnd/SSAFY_gitcat-local-llm to start chatting
- Pi new
How to use shsgrnd/SSAFY_gitcat-local-llm with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf shsgrnd/SSAFY_gitcat-local-llm:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "shsgrnd/SSAFY_gitcat-local-llm:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use shsgrnd/SSAFY_gitcat-local-llm with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf shsgrnd/SSAFY_gitcat-local-llm:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default shsgrnd/SSAFY_gitcat-local-llm:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use shsgrnd/SSAFY_gitcat-local-llm with Docker Model Runner:
docker model run hf.co/shsgrnd/SSAFY_gitcat-local-llm:Q4_K_M
- Lemonade
How to use shsgrnd/SSAFY_gitcat-local-llm with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull shsgrnd/SSAFY_gitcat-local-llm:Q4_K_M
Run and chat with the model
lemonade run user.SSAFY_gitcat-local-llm-Q4_K_M
List all available models
lemonade list
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf shsgrnd/SSAFY_gitcat-local-llm:Q4_K_M# Run inference directly in the terminal:
llama-cli -hf shsgrnd/SSAFY_gitcat-local-llm:Q4_K_MUse 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 shsgrnd/SSAFY_gitcat-local-llm:Q4_K_M# Run inference directly in the terminal:
./llama-cli -hf shsgrnd/SSAFY_gitcat-local-llm:Q4_K_MBuild 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 shsgrnd/SSAFY_gitcat-local-llm:Q4_K_M# Run inference directly in the terminal:
./build/bin/llama-cli -hf shsgrnd/SSAFY_gitcat-local-llm:Q4_K_MUse Docker
docker model run hf.co/shsgrnd/SSAFY_gitcat-local-llm:Q4_K_MGitCat Local LLM (GGUF)
GitCat은 생성형 AI 코딩 환경에서 안전한 작업 관리와 Git workflow 추천 자동화를 돕기 위해 만든 프로젝트입니다.
이 리포지토리는 그중 로컬 추론용 GGUF 모델을 배포하기 위한 공간이며, VS Code Extension 환경에서 아래 작업을 보조하도록 실험한 결과물을 담고 있습니다.
- 브랜치명 추천
- 커밋 메시지 추천
- PR 설명 추천
- 변경 맥락 기반 요약
이 모델은 외부 API 없이 로컬에서 실행 가능한 개발 보조 모델을 목표로 정리되었습니다.
Files
1. gitcat-v3-sft-merged-Q4_K_M.gguf
- SFT(Supervised Fine-Tuning) 기반 모델
- 현재 기준으로 가장 안정적인 추천 품질을 보인 기본 추천 모델
2. gitcat-v3-dpo-merged-Q4_K_M.gguf
- DPO(Direct Preference Optimization) 기반 모델
- preference pair를 반영한 추가 정렬 실험 버전
Recommended Model
처음 사용할 때는 아래 순서를 권장합니다.
gitcat-v3-sft-merged-Q4_K_M.ggufgitcat-v3-dpo-merged-Q4_K_M.gguf
현재 실험 기준에서는 SFT 버전이 더 안정적인 결과를 보였고, DPO 버전은 추가 튜닝 여지가 있는 비교 실험 모델입니다.
Base Model
- Base model:
Qwen/Qwen2.5-Coder-7B-Instruct - Format:
GGUF - Quantization:
Q4_K_M
Quick Start
Option 1. llama.cpp로 바로 실행
./llama-cli -m ./gitcat-v3-sft-merged-Q4_K_M.gguf
- Downloads last month
- 102
4-bit
Install from brew
# Start a local OpenAI-compatible server with a web UI: llama-server -hf shsgrnd/SSAFY_gitcat-local-llm:Q4_K_M# Run inference directly in the terminal: llama-cli -hf shsgrnd/SSAFY_gitcat-local-llm:Q4_K_M