Instructions to use choonok/VetJarvis-1.1-4B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use choonok/VetJarvis-1.1-4B-Instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="choonok/VetJarvis-1.1-4B-Instruct-GGUF",
	filename="VetJarvis-1.1-4B-Instruct-bf16.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 choonok/VetJarvis-1.1-4B-Instruct-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf choonok/VetJarvis-1.1-4B-Instruct-GGUF:BF16
# Run inference directly in the terminal:
llama-cli -hf choonok/VetJarvis-1.1-4B-Instruct-GGUF:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf choonok/VetJarvis-1.1-4B-Instruct-GGUF:BF16
# Run inference directly in the terminal:
llama-cli -hf choonok/VetJarvis-1.1-4B-Instruct-GGUF:BF16

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 choonok/VetJarvis-1.1-4B-Instruct-GGUF:BF16
# Run inference directly in the terminal:
./llama-cli -hf choonok/VetJarvis-1.1-4B-Instruct-GGUF:BF16

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 choonok/VetJarvis-1.1-4B-Instruct-GGUF:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf choonok/VetJarvis-1.1-4B-Instruct-GGUF:BF16

Use Docker

docker model run hf.co/choonok/VetJarvis-1.1-4B-Instruct-GGUF:BF16

LM Studio
Jan

vLLM

How to use choonok/VetJarvis-1.1-4B-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "choonok/VetJarvis-1.1-4B-Instruct-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": "choonok/VetJarvis-1.1-4B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/choonok/VetJarvis-1.1-4B-Instruct-GGUF:BF16

Ollama
How to use choonok/VetJarvis-1.1-4B-Instruct-GGUF with Ollama:
```
ollama run hf.co/choonok/VetJarvis-1.1-4B-Instruct-GGUF:BF16
```

Unsloth Studio

How to use choonok/VetJarvis-1.1-4B-Instruct-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 choonok/VetJarvis-1.1-4B-Instruct-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 choonok/VetJarvis-1.1-4B-Instruct-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for choonok/VetJarvis-1.1-4B-Instruct-GGUF to start chatting

How to use choonok/VetJarvis-1.1-4B-Instruct-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf choonok/VetJarvis-1.1-4B-Instruct-GGUF:BF16

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": "choonok/VetJarvis-1.1-4B-Instruct-GGUF:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use choonok/VetJarvis-1.1-4B-Instruct-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf choonok/VetJarvis-1.1-4B-Instruct-GGUF:BF16

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 choonok/VetJarvis-1.1-4B-Instruct-GGUF:BF16

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use choonok/VetJarvis-1.1-4B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/choonok/VetJarvis-1.1-4B-Instruct-GGUF:BF16
```

Lemonade

How to use choonok/VetJarvis-1.1-4B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull choonok/VetJarvis-1.1-4B-Instruct-GGUF:BF16

Run and chat with the model

lemonade run user.VetJarvis-1.1-4B-Instruct-GGUF-BF16

List all available models

lemonade list

VetJarvis-1.1-4B-Instruct-GGUF / README.md

choonok

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	---
	license: other
	license_name: vetjarvis-model-license-1.0-nc
	license_link: LICENSE
	language:
	- ko
	- en
	base_model:
	- choonok/VetJarvis-1.1-4B-Instruct
	base_model_relation: quantized
	pipeline_tag: text-generation
	library_name: gguf
	tags:
	- veterinary
	- companion-animal
	- canine
	- feline
	- medical
	- domain-specific
	- qwen3.5
	- gguf
	- llama.cpp
	- not-a-medical-device
	---

	# VetJarvis 1.1-4B-Instruct (GGUF)

	[choonok/VetJarvis-1.1-4B-Instruct](https://huggingface.co/choonok/VetJarvis-1.1-4B-Instruct)를 GGUF 포맷으로 변환한 버전입니다.

	llama.cpp, Ollama, LM Studio 등 로컬 추론 도구에서 사용할 수 있습니다.

	This is a GGUF-converted version of [choonok/VetJarvis-1.1-4B-Instruct](https://huggingface.co/choonok/VetJarvis-1.1-4B-Instruct), suitable for local inference with llama.cpp, Ollama, LM Studio, etc.

	## 제공 파일 / Files

	\| 파일 \| 양자화 \| 크기 \| 권장 용도 \|
	\|------\|--------\|------\|----------\|
	\| `VetJarvis-1.1-4B-Instruct-bf16.gguf` \| BF16 \| ~7.9 GB \| 정확도 우선, 서버, GPU 16GB+ \|
	\| `VetJarvis-1.1-4B-Instruct-q8_0.gguf` \| Q8_0 \| ~4.2 GB \| 거의 무손실, 일반 사용 권장 \|

	## 추천 추론 파라미터 / Recommended Inference Parameters

	\| 파라미터 \| 값 \|
	\|---------\|---\|
	\| Temperature \| 0.8 \|
	\| Top-p \| 0.9 \|
	\| Max Tokens \| 32,768 \|
	\| Context Length \| ≤ 262,144 \|
	\| enable_thinking \| True (권장) \|

	## 사용법 / Usage

	### llama.cpp

	```bash
	./build/bin/llama-cli \
	-m VetJarvis-1.1-4B-Instruct-q8_0.gguf \
	--jinja \
	-ngl 99 \
	-sys "당신은 한국 수의사를 보조하는 AI 어시스턴트입니다. 반드시 한국어로 답변하세요." \
	-p "고양이 만성 신부전의 초기 증상은?" \
	-n 32768 \
	--temp 0.8 \
	--top-p 0.9
	```

	### Ollama

	```
	FROM ./VetJarvis-1.1-4B-Instruct-q8_0.gguf
	PARAMETER temperature 0.8
	PARAMETER top_p 0.9
	PARAMETER num_ctx 32768
	PARAMETER stop "<\|im_end\|>"
	```

	```bash
	ollama create vetjarvis-1.1-4b-instruct -f Modelfile
	ollama run vetjarvis-1.1-4b-instruct
	```

	### LM Studio

	추후 사용법 가이드를 추가할 예정입니다. _Detailed LM Studio guide will be added later._

	## 변환 정보 / Conversion Details

	- 변환 도구: [llama.cpp](https://github.com/ggerganov/llama.cpp) `convert_hf_to_gguf.py`
	- 원본 정밀도: BF16 (Qwen3.5-4B는 BF16으로 학습됨)
	- 변환 시 BF16 → BF16 직접 변환 (정밀도 손실 없음)
	- Q8_0은 원본에서 직접 양자화 생성

	## 모델 아키텍처 / Architecture Note

	이 모델은 Qwen3.5의 Transformer + SSM 하이브리드 아키텍처입니다. 256K 토큰의 긴 컨텍스트를 지원하며, llama.cpp에서 정상 동작이 확인되었습니다.

	q4_K_M 같은 저비트 양자화는 SSM 레이어 손실이 일반 Transformer 모델보다 클 수 있으므로, BF16 또는 Q8_0 사용을 권장합니다.

	## 라이선스 / License

	원본 모델의 라이선스(`vetjarvis-model-license-1.0-nc`)를 그대로 따릅니다. 비상업적 용도로만 사용 가능합니다. 자세한 내용은 동봉된 [LICENSE](LICENSE) 파일을 참고하세요.

	This GGUF version inherits the original `vetjarvis-model-license-1.0-nc` license. Non-commercial use only. See the included [LICENSE](LICENSE) file for details.

	## ⚠️ 의료기기 아님 / Not a Medical Device

	본 모델은 임상 의사결정을 보조하는 참고 도구이며, 진단/처방을 대체하지 않습니다. 모든 임상 판단은 자격을 갖춘 수의사가 수행해야 합니다.

	This model is a reference tool to support clinical decision-making. It is not a medical device and does not replace diagnosis or prescription by a qualified veterinarian.