Instructions to use KuroTo4ka/Qwen3-VL-8B-Instruct-Unredacted-MAX-Quants-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use KuroTo4ka/Qwen3-VL-8B-Instruct-Unredacted-MAX-Quants-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 KuroTo4ka/Qwen3-VL-8B-Instruct-Unredacted-MAX-Quants-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf KuroTo4ka/Qwen3-VL-8B-Instruct-Unredacted-MAX-Quants-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 KuroTo4ka/Qwen3-VL-8B-Instruct-Unredacted-MAX-Quants-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf KuroTo4ka/Qwen3-VL-8B-Instruct-Unredacted-MAX-Quants-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf KuroTo4ka/Qwen3-VL-8B-Instruct-Unredacted-MAX-Quants-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf KuroTo4ka/Qwen3-VL-8B-Instruct-Unredacted-MAX-Quants-GGUF:Q4_K_M

Use Docker

docker model run hf.co/KuroTo4ka/Qwen3-VL-8B-Instruct-Unredacted-MAX-Quants-GGUF:Q4_K_M

Llama new
LM Studio
Jan
Ollama
How to use KuroTo4ka/Qwen3-VL-8B-Instruct-Unredacted-MAX-Quants-GGUF with Ollama:
```
ollama run hf.co/KuroTo4ka/Qwen3-VL-8B-Instruct-Unredacted-MAX-Quants-GGUF:Q4_K_M
```
Unsloth Desktop

How to use KuroTo4ka/Qwen3-VL-8B-Instruct-Unredacted-MAX-Quants-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf KuroTo4ka/Qwen3-VL-8B-Instruct-Unredacted-MAX-Quants-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": "KuroTo4ka/Qwen3-VL-8B-Instruct-Unredacted-MAX-Quants-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use KuroTo4ka/Qwen3-VL-8B-Instruct-Unredacted-MAX-Quants-GGUF with Docker Model Runner:
```
docker model run hf.co/KuroTo4ka/Qwen3-VL-8B-Instruct-Unredacted-MAX-Quants-GGUF:Q4_K_M
```

Lemonade

How to use KuroTo4ka/Qwen3-VL-8B-Instruct-Unredacted-MAX-Quants-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull KuroTo4ka/Qwen3-VL-8B-Instruct-Unredacted-MAX-Quants-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3-VL-8B-Instruct-Unredacted-MAX-Quants-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use KuroTo4ka/Qwen3-VL-8B-Instruct-Unredacted-MAX-Quants-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 KuroTo4ka/Qwen3-VL-8B-Instruct-Unredacted-MAX-Quants-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 KuroTo4ka/Qwen3-VL-8B-Instruct-Unredacted-MAX-Quants-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use KuroTo4ka/Qwen3-VL-8B-Instruct-Unredacted-MAX-Quants-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf KuroTo4ka/Qwen3-VL-8B-Instruct-Unredacted-MAX-Quants-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 "KuroTo4ka/Qwen3-VL-8B-Instruct-Unredacted-MAX-Quants-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"

Qwen3-VL-8B-Instruct-Unredacted-MAX-Quants-GGUF

This repository contains high-quality GGUF quantizations for the prithivMLmods/Qwen3-VL-8B-Instruct-Unredacted-MAX model.

Highlights

Unredacted & MAX: Maximum performance version without restrictive filters.
Full Vision Support: Includes multiple versions of the vision projector (mmproj) for different hardware needs.
Optimized: Compatible with the latest llama.cpp and other GGUF-supported backends.

Files Included

1. Model Weights (LLM)

Filename	Quant Method	Description
`Q4_K_M.gguf`	Q4_K_M	Recommended. Best balance of speed and intelligence.
`Q8_0.gguf`	Q8_0	High quality, nearly identical to original weights.
`Q6_K.gguf`	Q6_K	Very high quality, slightly slower than Q4.
`Q5_K_M.gguf`	Q5_K_M	Good balance between Q4 and Q6.
`Q3_K_M.gguf`	Q3_K_M	Low size, moderate quality loss.
`Q2_K.gguf`	Q2_K	Smallest possible size, significant quality loss.
`F16.gguf`	F16	Baseline reference quality.

2. Vision Projectors (mmproj)

Required for image recognition tasks.

Filename	Type	Description
`mmproj-f32.gguf`	F32	Absolute maximum precision (2.3GB).
`mmproj-f16.gguf`	F16	Industry standard for high-quality vision.
`mmproj-bf16.gguf`	BF16	Optimized for modern NVIDIA GPUs (Ampere+).
`mmproj-q8_0.gguf`	Q8_0	Best for saving VRAM without losing recognition detail.

Usage

To use vision capabilities in llama.cpp, use the following command:

./llama-cli -m Qwen3-VL-8B-Instruct-Unredacted-MAX.Q4_K_M.gguf \
            --mmproj Qwen3-VL-8B-Instruct-Unredacted-MAX.mmproj-f16.gguf \
            --image path/to/your/image.jpg \
            -p "Describe this image"

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GGUF

Model size

8B params

Architecture

qwen3vl

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for KuroTo4ka/Qwen3-VL-8B-Instruct-Unredacted-MAX-Quants-GGUF

Base model

Qwen/Qwen3-VL-8B-Instruct

Finetuned

prithivMLmods/Qwen3-VL-8B-Instruct-Unredacted-MAX

Quantized

(6)

this model