Instructions to use prithivMLmods/Jan-v1-AIO-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Jan-v1-AIO-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="prithivMLmods/Jan-v1-AIO-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("prithivMLmods/Jan-v1-AIO-GGUF", dtype="auto")

llama-cpp-python

How to use prithivMLmods/Jan-v1-AIO-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="prithivMLmods/Jan-v1-AIO-GGUF",
	filename="Jan-v1-2509-GGUF/Jan-v1-2509.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 prithivMLmods/Jan-v1-AIO-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf prithivMLmods/Jan-v1-AIO-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/Jan-v1-AIO-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf prithivMLmods/Jan-v1-AIO-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/Jan-v1-AIO-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 prithivMLmods/Jan-v1-AIO-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/Jan-v1-AIO-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 prithivMLmods/Jan-v1-AIO-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/Jan-v1-AIO-GGUF:Q4_K_M

Use Docker

docker model run hf.co/prithivMLmods/Jan-v1-AIO-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use prithivMLmods/Jan-v1-AIO-GGUF with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/prithivMLmods/Jan-v1-AIO-GGUF:Q4_K_M

SGLang

How to use prithivMLmods/Jan-v1-AIO-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 "prithivMLmods/Jan-v1-AIO-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": "prithivMLmods/Jan-v1-AIO-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 "prithivMLmods/Jan-v1-AIO-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": "prithivMLmods/Jan-v1-AIO-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use prithivMLmods/Jan-v1-AIO-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/Jan-v1-AIO-GGUF:Q4_K_M
```

Unsloth Studio

How to use prithivMLmods/Jan-v1-AIO-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 prithivMLmods/Jan-v1-AIO-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 prithivMLmods/Jan-v1-AIO-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for prithivMLmods/Jan-v1-AIO-GGUF to start chatting

How to use prithivMLmods/Jan-v1-AIO-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf prithivMLmods/Jan-v1-AIO-GGUF: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": "prithivMLmods/Jan-v1-AIO-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use prithivMLmods/Jan-v1-AIO-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 prithivMLmods/Jan-v1-AIO-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 prithivMLmods/Jan-v1-AIO-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use prithivMLmods/Jan-v1-AIO-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Jan-v1-AIO-GGUF:Q4_K_M
```

Lemonade

How to use prithivMLmods/Jan-v1-AIO-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/Jan-v1-AIO-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Jan-v1-AIO-GGUF-Q4_K_M

List all available models

lemonade list

How to use from the

Use from the

llama-cpp-python library

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="prithivMLmods/Jan-v1-AIO-GGUF",
	filename="",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Jan-v1-AIO-GGUF

Jan-v1-4B is a 4-billion-parameter language model built on the Qwen3-4B-thinking architecture, meticulously fine-tuned for agentic reasoning, problem-solving, and tool utilization with support for web search tasks and large context lengths up to 256,000 tokens. Achieving 91.1% accuracy on the SimpleQA benchmark, Jan-v1-4B excels at factual question answering and conversation while running efficiently on local hardware for enhanced privacy and offline use, making it a strong choice for advanced Q&A, reasoning, and integration with the Jan desktop application or compatible inference engines. Jan-v1-edge is a lightweight agentic model built for fast, reliable on-device execution. As the second release in the Jan Family, it is distilled from the larger Jan-v1 model, preserving strong reasoning and problem-solving ability in a smaller footprint suitable for resource-constrained environments. Jan-v1-edge was developed through a two-phase post-training process. The first phase, Supervised Fine-Tuning (SFT), transferred core capabilities from the Jan-v1 teacher model to the smaller student. The second phase, Reinforcement Learning with Verifiable Rewards (RLVR) —the same method used in Jan-v1 and Lucy—further optimized reasoning efficiency, tool use, and correctness. This staged approach delivers reliable results on complex, interactive workloads.

Jan-v1 GGUF Models

Model Name	Hugging Face Link
Jan-v1-2509-GGUF	🔗 Link
Jan-v1-edge-GGUF	🔗 Link
Jan-v1-4B-GGUF	🔗 Link

Model Files

Jan-v1-2509

File Name	Quant Type	File Size
Jan-v1-2509.BF16.gguf	BF16	8.05 GB
Jan-v1-2509.F16.gguf	F16	8.05 GB
Jan-v1-2509.F32.gguf	F32	16.1 GB
Jan-v1-2509.Q2_K.gguf	Q2_K	1.67 GB
Jan-v1-2509.Q3_K_L.gguf	Q3_K_L	2.24 GB
Jan-v1-2509.Q3_K_M.gguf	Q3_K_M	2.08 GB
Jan-v1-2509.Q3_K_S.gguf	Q3_K_S	1.89 GB
Jan-v1-2509.Q4_K_M.gguf	Q4_K_M	2.5 GB
Jan-v1-2509.Q4_K_S.gguf	Q4_K_S	2.38 GB
Jan-v1-2509.Q5_K_M.gguf	Q5_K_M	2.89 GB
Jan-v1-2509.Q5_K_S.gguf	Q5_K_S	2.82 GB
Jan-v1-2509.Q6_K.gguf	Q6_K	3.31 GB
Jan-v1-2509.Q8_0.gguf	Q8_0	4.28 GB

Jan-v1-edge

File Name	Quant Type	File Size
Jan-v1-edge.BF16.gguf	BF16	3.45 GB
Jan-v1-edge.F16.gguf	F16	3.45 GB
Jan-v1-edge.F32.gguf	F32	6.89 GB
Jan-v1-edge.Q2_K.gguf	Q2_K	778 MB
Jan-v1-edge.Q3_K_L.gguf	Q3_K_L	1 GB
Jan-v1-edge.Q3_K_M.gguf	Q3_K_M	940 MB
Jan-v1-edge.Q3_K_S.gguf	Q3_K_S	867 MB
Jan-v1-edge.Q4_0.gguf	Q4_0	1.05 GB
Jan-v1-edge.Q4_1.gguf	Q4_1	1.14 GB
Jan-v1-edge.Q4_K.gguf	Q4_K	1.11 GB
Jan-v1-edge.Q4_K_M.gguf	Q4_K_M	1.11 GB
Jan-v1-edge.Q4_K_S.gguf	Q4_K_S	1.06 GB
Jan-v1-edge.Q5_0.gguf	Q5_0	1.23 GB
Jan-v1-edge.Q5_1.gguf	Q5_1	1.32 GB
Jan-v1-edge.Q5_K.gguf	Q5_K	1.26 GB
Jan-v1-edge.Q5_K_M.gguf	Q5_K_M	1.26 GB
Jan-v1-edge.Q5_K_S.gguf	Q5_K_S	1.23 GB
Jan-v1-edge.Q6_K.gguf	Q6_K	1.42 GB
Jan-v1-edge.Q8_0.gguf	Q8_0	1.83 GB

Jan-v1-4B

File Name	Quant Type	File Size
Jan-v1-4B.BF16.gguf	BF16	8.05 GB
Jan-v1-4B.F16.gguf	F16	8.05 GB
Jan-v1-4B.F32.gguf	F32	16.1 GB
Jan-v1-4B.Q2_K.gguf	Q2_K	1.67 GB
Jan-v1-4B.Q3_K_L.gguf	Q3_K_L	2.24 GB
Jan-v1-4B.Q3_K_M.gguf	Q3_K_M	2.08 GB
Jan-v1-4B.Q3_K_S.gguf	Q3_K_S	1.89 GB
Jan-v1-4B.Q4_K_M.gguf	Q4_K_M	2.5 GB
Jan-v1-4B.Q4_K_S.gguf	Q4_K_S	2.38 GB
Jan-v1-4B.Q5_K_M.gguf	Q5_K_M	2.89 GB
Jan-v1-4B.Q5_K_S.gguf	Q5_K_S	2.82 GB
Jan-v1-4B.Q6_K.gguf	Q6_K	3.31 GB
Jan-v1-4B.Q8_0.gguf	Q8_0	4.28 GB

Quants Usage

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):

Downloads last month: 501

GGUF

Model size

4B params

Architecture

qwen3

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

32-bit

Model tree for prithivMLmods/Jan-v1-AIO-GGUF

Base model

Qwen/Qwen3-4B-Thinking-2507

Finetuned

janhq/Jan-v1-2509

Quantized

(6)

this model