Instructions to use megatharun/Weld-Defects-Qwen3-VL-8B-Instruct-Q4_K_M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use megatharun/Weld-Defects-Qwen3-VL-8B-Instruct-Q4_K_M 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 megatharun/Weld-Defects-Qwen3-VL-8B-Instruct-Q4_K_M:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf megatharun/Weld-Defects-Qwen3-VL-8B-Instruct-Q4_K_M:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf megatharun/Weld-Defects-Qwen3-VL-8B-Instruct-Q4_K_M:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf megatharun/Weld-Defects-Qwen3-VL-8B-Instruct-Q4_K_M: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 megatharun/Weld-Defects-Qwen3-VL-8B-Instruct-Q4_K_M:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf megatharun/Weld-Defects-Qwen3-VL-8B-Instruct-Q4_K_M: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 megatharun/Weld-Defects-Qwen3-VL-8B-Instruct-Q4_K_M:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf megatharun/Weld-Defects-Qwen3-VL-8B-Instruct-Q4_K_M:Q4_K_M

Use Docker

docker model run hf.co/megatharun/Weld-Defects-Qwen3-VL-8B-Instruct-Q4_K_M:Q4_K_M

LM Studio
Jan
Ollama
How to use megatharun/Weld-Defects-Qwen3-VL-8B-Instruct-Q4_K_M with Ollama:
```
ollama run hf.co/megatharun/Weld-Defects-Qwen3-VL-8B-Instruct-Q4_K_M:Q4_K_M
```

Unsloth Studio

How to use megatharun/Weld-Defects-Qwen3-VL-8B-Instruct-Q4_K_M 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 megatharun/Weld-Defects-Qwen3-VL-8B-Instruct-Q4_K_M 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 megatharun/Weld-Defects-Qwen3-VL-8B-Instruct-Q4_K_M to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for megatharun/Weld-Defects-Qwen3-VL-8B-Instruct-Q4_K_M to start chatting

How to use megatharun/Weld-Defects-Qwen3-VL-8B-Instruct-Q4_K_M with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf megatharun/Weld-Defects-Qwen3-VL-8B-Instruct-Q4_K_M: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": "megatharun/Weld-Defects-Qwen3-VL-8B-Instruct-Q4_K_M:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use megatharun/Weld-Defects-Qwen3-VL-8B-Instruct-Q4_K_M with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf megatharun/Weld-Defects-Qwen3-VL-8B-Instruct-Q4_K_M: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 "megatharun/Weld-Defects-Qwen3-VL-8B-Instruct-Q4_K_M: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"

Docker Model Runner
How to use megatharun/Weld-Defects-Qwen3-VL-8B-Instruct-Q4_K_M with Docker Model Runner:
```
docker model run hf.co/megatharun/Weld-Defects-Qwen3-VL-8B-Instruct-Q4_K_M:Q4_K_M
```

Lemonade

How to use megatharun/Weld-Defects-Qwen3-VL-8B-Instruct-Q4_K_M with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull megatharun/Weld-Defects-Qwen3-VL-8B-Instruct-Q4_K_M:Q4_K_M

Run and chat with the model

lemonade run user.Weld-Defects-Qwen3-VL-8B-Instruct-Q4_K_M-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use megatharun/Weld-Defects-Qwen3-VL-8B-Instruct-Q4_K_M with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf megatharun/Weld-Defects-Qwen3-VL-8B-Instruct-Q4_K_M: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 megatharun/Weld-Defects-Qwen3-VL-8B-Instruct-Q4_K_M:Q4_K_M

Run Hermes

hermes

Atomic Chat

Agentic LLM (Qwen3) for weld defect detection in industrial radiographic image

Here we build a simple multi-system prompts that used Qwen3-VL-8B-Instruct-Q4_K_M.gguf and mmproj-F16.gguf with lovely results without fine-tuning the model. Tuning were only done on the multi-system prompts but the images used for testing were CLAHE improved. The inference file can be used to detect and classify weld defects in industrial radiographic image.

This development is part of effort at NDT-Material Structural Integrity Group at Agensi Nuklear Malaysia to build agentic AI for NDT inspection.

Simple inference file: LLM_Inference.ipynb

System prompt files (decision tree agents): prompt_router_3.txt, prompt_volumetric_3.txt, prompt_planar_3.txt

All these files (including the model) will be updated from time to time when better results and system are achieved.

Downloads last month: 81

GGUF

Model size

8B params

Architecture

qwen3vl

Hardware compatibility

4-bit

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