Instructions to use prithivMLmods/Jan-v2-VL-max-FP8-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Jan-v2-VL-max-FP8-GGUF with Transformers:

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

pipe = pipeline("image-text-to-text", model="prithivMLmods/Jan-v2-VL-max-FP8-GGUF")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("prithivMLmods/Jan-v2-VL-max-FP8-GGUF", dtype="auto")

llama-cpp-python

How to use prithivMLmods/Jan-v2-VL-max-FP8-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="prithivMLmods/Jan-v2-VL-max-FP8-GGUF",
	filename="Jan-v2-VL-max-FP8.Q8_0.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": [
				{
					"type": "text",
					"text": "Describe this image in one sentence."
				},
				{
					"type": "image_url",
					"image_url": {
						"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use prithivMLmods/Jan-v2-VL-max-FP8-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-v2-VL-max-FP8-GGUF:Q8_0
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/Jan-v2-VL-max-FP8-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf prithivMLmods/Jan-v2-VL-max-FP8-GGUF:Q8_0
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/Jan-v2-VL-max-FP8-GGUF:Q8_0

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-v2-VL-max-FP8-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/Jan-v2-VL-max-FP8-GGUF:Q8_0

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-v2-VL-max-FP8-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/Jan-v2-VL-max-FP8-GGUF:Q8_0

Use Docker

docker model run hf.co/prithivMLmods/Jan-v2-VL-max-FP8-GGUF:Q8_0

LM Studio
Jan

vLLM

How to use prithivMLmods/Jan-v2-VL-max-FP8-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Jan-v2-VL-max-FP8-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-v2-VL-max-FP8-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/Jan-v2-VL-max-FP8-GGUF:Q8_0

SGLang

How to use prithivMLmods/Jan-v2-VL-max-FP8-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-v2-VL-max-FP8-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-v2-VL-max-FP8-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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-v2-VL-max-FP8-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-v2-VL-max-FP8-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Ollama
How to use prithivMLmods/Jan-v2-VL-max-FP8-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/Jan-v2-VL-max-FP8-GGUF:Q8_0
```

Unsloth Studio new

How to use prithivMLmods/Jan-v2-VL-max-FP8-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-v2-VL-max-FP8-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-v2-VL-max-FP8-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-v2-VL-max-FP8-GGUF to start chatting

Pi new

How to use prithivMLmods/Jan-v2-VL-max-FP8-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-v2-VL-max-FP8-GGUF:Q8_0

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-v2-VL-max-FP8-GGUF:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use prithivMLmods/Jan-v2-VL-max-FP8-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-v2-VL-max-FP8-GGUF:Q8_0

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-v2-VL-max-FP8-GGUF:Q8_0

Run Hermes

hermes

Docker Model Runner
How to use prithivMLmods/Jan-v2-VL-max-FP8-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Jan-v2-VL-max-FP8-GGUF:Q8_0
```

Lemonade

How to use prithivMLmods/Jan-v2-VL-max-FP8-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/Jan-v2-VL-max-FP8-GGUF:Q8_0

Run and chat with the model

lemonade run user.Jan-v2-VL-max-FP8-GGUF-Q8_0

List all available models

lemonade list

Jan-v2-VL-max-FP8-GGUF

Jan-v2-VL-max-FP8 from janhq is a 30B-parameter vision-language model extending the Jan-v2-VL family, fine-tuned from Qwen3-VL-30B-A3B-Thinking using LoRA-based RLVR to enhance long-horizon execution stability with minimal error accumulation over many steps, evaluated on "The Illusion of Diminishing Returns" benchmark emphasizing execution length over recall. Optimized for agentic automation and UI control tasks where plans/knowledge are provided upfront—such as stepwise browser/desktop operations with screenshot grounding and Jan Browser MCP tool calls—it shows no regressions and small gains versus its base on standard tasks, with largest improvements in extended sequences, while FP8 quantization reduces memory/latency for vLLM 0.12.0 deployment (transformers==4.57.1, llm-compressor) using parameters like temperature=1.0, top_p=0.95, presence_penalty=1.5. Hosted on Jan Web (chat.jan.ai) for immediate use, it supports --enable-auto-tool-choice and Hermes/DeepSeek_R1 parsers for production-ready multimodal agents in long-context scenarios.

Jan-v2-VL-max-FP8 [GGUF]

File Name	Quant Type	File Size	File Link
Jan-v2-VL-max-FP8.Q8_0.gguf	Q8_0	32.5 GB	Download
Jan-v2-VL-max-FP8.mmproj-q8_0.gguf	mmproj-q8_0	712 MB	Download

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: 72

GGUF

Model size

31B params

Architecture

qwen3vlmoe

Hardware compatibility

8-bit

Model tree for prithivMLmods/Jan-v2-VL-max-FP8-GGUF

Base model

Qwen/Qwen3-VL-30B-A3B-Thinking

Quantized

janhq/Jan-v2-VL-max-FP8

Quantized

(4)

this model