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

choonok commited on Apr 30

Commit

c9580bd

verified ·

1 Parent(s): 6e29ea2

Upload README.md with huggingface_hub

Browse files

Files changed (1) hide show

README.md +13 -10

README.md CHANGED Viewed

@@ -87,9 +87,19 @@ LM Studio는 GUI 기반 로컬 LLM 도구로, 비개발자도 쉽게 모델을
 | **컨텍스트 길이 (Context Length)** | 8192 ~ 32768 | 길수록 메모리 사용량 증가. 모델은 최대 262,144 지원 |
 | **GPU 오프로딩 (GPU Offload)** | 32 (전체) | 모든 레이어를 GPU에 올림. VRAM 부족 시 줄임 |
-### 5. 추론 파라미터 설정
-우측 상단 **⚙ 설정** 또는 채팅 사이드바에서 다음 값을 적용합니다.
 | 파라미터 | 값 |
 |---------|---|
@@ -97,14 +107,7 @@ LM Studio는 GUI 기반 로컬 LLM 도구로, 비개발자도 쉽게 모델을
 | Top-p | **0.9** |
 | Max Tokens | 32768 |
-### 6. System Prompt 입력
-```
-당신은 'VetJarvis'입니다. 한국 수의사를 보조하는 임상 지원 AI 어시스턴트로,
-모든 답변은 반드시 한국어로 작성하세요.
-```
-### 7. 채팅하기
 질문을 입력하면 thinking 과정(`Thought for X seconds`)을 거쳐 한국어로 답변합니다.

 | **컨텍스트 길이 (Context Length)** | 8192 ~ 32768 | 길수록 메모리 사용량 증가. 모델은 최대 262,144 지원 |
 | **GPU 오프로딩 (GPU Offload)** | 32 (전체) | 모든 레이어를 GPU에 올림. VRAM 부족 시 줄임 |
+### 5. System Prompt 및 추론 파라미터 설정
+우측 상단의 **▭ 사이드바 토글** 아이콘을 클릭하거나 **Ctrl + E** 단축키로 우측 패널을 펼치면, **시스템 프롬프트** 영역과 **Model Parameters** 설정이 나타납니다.
+**시스템 프롬프트** 텍스트 영역에 다음 내용을 붙여넣으세요.
+```
+당신은 'VetJarvis'입니다.
+한국 수의사를 보조하는 임상 지원 AI 어시스턴트로,
+모든 답변은 반드시 한국어로 작성하세요.
+```
+같은 우측 패널에서 추론 파라미터도 조정할 수 있습니다.
 | 파라미터 | 값 |
 |---------|---|
 | Top-p | **0.9** |
 | Max Tokens | 32768 |
+### 6. 채팅하기
 질문을 입력하면 thinking 과정(`Thought for X seconds`)을 거쳐 한국어로 답변합니다.