Instructions to use janhq/Jan-v1-2509-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use janhq/Jan-v1-2509-gguf with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="janhq/Jan-v1-2509-gguf")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("janhq/Jan-v1-2509-gguf", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use janhq/Jan-v1-2509-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 janhq/Jan-v1-2509-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf janhq/Jan-v1-2509-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 janhq/Jan-v1-2509-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf janhq/Jan-v1-2509-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 janhq/Jan-v1-2509-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf janhq/Jan-v1-2509-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 janhq/Jan-v1-2509-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf janhq/Jan-v1-2509-gguf:Q4_K_M

Use Docker

docker model run hf.co/janhq/Jan-v1-2509-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use janhq/Jan-v1-2509-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "janhq/Jan-v1-2509-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": "janhq/Jan-v1-2509-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/janhq/Jan-v1-2509-gguf:Q4_K_M

SGLang

How to use janhq/Jan-v1-2509-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 "janhq/Jan-v1-2509-gguf" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "janhq/Jan-v1-2509-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "janhq/Jan-v1-2509-gguf" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "janhq/Jan-v1-2509-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use janhq/Jan-v1-2509-gguf with Ollama:
```
ollama run hf.co/janhq/Jan-v1-2509-gguf:Q4_K_M
```
Unsloth Desktop

How to use janhq/Jan-v1-2509-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf janhq/Jan-v1-2509-gguf:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "janhq/Jan-v1-2509-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use janhq/Jan-v1-2509-gguf with Docker Model Runner:
```
docker model run hf.co/janhq/Jan-v1-2509-gguf:Q4_K_M
```

Lemonade

How to use janhq/Jan-v1-2509-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull janhq/Jan-v1-2509-gguf:Q4_K_M

Run and chat with the model

lemonade run user.Jan-v1-2509-gguf-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use janhq/Jan-v1-2509-gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf janhq/Jan-v1-2509-gguf: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 janhq/Jan-v1-2509-gguf:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use janhq/Jan-v1-2509-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf janhq/Jan-v1-2509-gguf:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "janhq/Jan-v1-2509-gguf:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Jan-v1: Advanced Agentic Language Model

Overview

Update: Jan-v1-2509

We have released a small weight update, jan-v1-2509, which refines the original v1.

No architectural changes.
Slightly lower performance on SimpleQA compared to jan-v1.
Slightly mproved results on other chat benchmarks and overall more reliable

Jan-v1 is the first release in the Jan Family, designed for agentic reasoning and problem-solving within the Jan App. Based on our Lucy model, Jan-v1 achieves improved performance through model scaling.

Jan-v1 uses the Qwen3-4B-thinking model to provide enhanced reasoning capabilities and tool utilization. This architecture delivers better performance on complex agentic tasks.

Performance

Question Answering (SimpleQA)

For question-answering, Jan-v1 shows a significant performance gain from model scaling, achieving 91.1% accuracy.

The 91.1% SimpleQA accuracy with Jan-v1 remains a highlight, though Jan-v1-2509 focuses on balancing factual QA with improved reliability across chat-based reasoning tasks.

Chat Benchmarks

These benchmarks evaluate the model's conversational and instructional capabilities.

Quick Start

Integration with Jan App

Jan-v1 is optimized for direct integration with the Jan App. Simply select the model from the Jan App interface for immediate access to its full capabilities.

Local Deployment

Using vLLM:

vllm serve janhq/Jan-v1-2509 \
    --host 0.0.0.0 \
    --port 1234 \
    --enable-auto-tool-choice \
    --tool-call-parser hermes

Using llama.cpp:

llama-server --model Jan-v1-2509-Q4_K_M.gguf \
    --host 0.0.0.0 \
    --port 1234 \
    --jinja \
    --no-context-shift

Recommended Parameters

temperature: 0.6
top_p: 0.95
top_k: 20
min_p: 0.0
max_tokens: 2048

🤝 Community & Support

Discussions: HuggingFace Community
Jan App: Learn more about the Jan App at jan.ai

(*) Note

By default we have system prompt in chat template, this is to make sure the model having the same performance with the benchmark result. You can also use the vanilla chat template without system prompt in the file chat_template_raw.jinja.

📄 Citation

Updated Soon

Downloads last month: 350

GGUF

Model size

4B params

Architecture

qwen3

Hardware compatibility

4-bit

5-bit

6-bit

8-bit

Model tree for janhq/Jan-v1-2509-gguf

Base model

Qwen/Qwen3-4B-Thinking-2507

Finetuned

janhq/Jan-v1-2509

Quantized

(6)

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Collection including janhq/Jan-v1-2509-gguf

Jan v1

Collection

Jan-v1 is the first release in the Jan Family, designed for agentic reasoning and problem-solving within the Jan App • 6 items • Updated Sep 10, 2025 • 8