Instructions to use shsgrnd/SSAFY_gitcat-local-llm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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 Settings

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

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

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

SSAFY_gitcat-local-llm / README.md

shsgrnd

Update README.md

eb336b1 verified 17 days ago

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	---
	license: apache-2.0
	base_model: Qwen/Qwen2.5-Coder-7B-Instruct
	tags:
	- gguf
	- llama.cpp
	- node-llama-cpp
	- code
	- git
	- recommendation
	- vscode-extension
	language:
	- ko
	- en
	pipeline_tag: text-generation
	---

	# GitCat 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.

	1. `gitcat-v3-sft-merged-Q4_K_M.gguf`
	2. `gitcat-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.cpp`로 바로 실행

	```bash
	./llama-cli -m ./gitcat-v3-sft-merged-Q4_K_M.gguf