Instructions to use digitranslab/Megamind-v2-VL-med-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use digitranslab/Megamind-v2-VL-med-gguf with Transformers:

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

pipe = pipeline("image-text-to-text", model="digitranslab/Megamind-v2-VL-med-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("digitranslab/Megamind-v2-VL-med-gguf", dtype="auto")

llama-cpp-python

How to use digitranslab/Megamind-v2-VL-med-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="digitranslab/Megamind-v2-VL-med-gguf",
	filename="Megamind-v2-VL-med-Q3_K_L.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 digitranslab/Megamind-v2-VL-med-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf digitranslab/Megamind-v2-VL-med-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf digitranslab/Megamind-v2-VL-med-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 digitranslab/Megamind-v2-VL-med-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf digitranslab/Megamind-v2-VL-med-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 digitranslab/Megamind-v2-VL-med-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf digitranslab/Megamind-v2-VL-med-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 digitranslab/Megamind-v2-VL-med-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf digitranslab/Megamind-v2-VL-med-gguf:Q4_K_M

Use Docker

docker model run hf.co/digitranslab/Megamind-v2-VL-med-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use digitranslab/Megamind-v2-VL-med-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "digitranslab/Megamind-v2-VL-med-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": "digitranslab/Megamind-v2-VL-med-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/digitranslab/Megamind-v2-VL-med-gguf:Q4_K_M

SGLang

How to use digitranslab/Megamind-v2-VL-med-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 "digitranslab/Megamind-v2-VL-med-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": "digitranslab/Megamind-v2-VL-med-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 "digitranslab/Megamind-v2-VL-med-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": "digitranslab/Megamind-v2-VL-med-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 digitranslab/Megamind-v2-VL-med-gguf with Ollama:
```
ollama run hf.co/digitranslab/Megamind-v2-VL-med-gguf:Q4_K_M
```

Unsloth Studio new

How to use digitranslab/Megamind-v2-VL-med-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 digitranslab/Megamind-v2-VL-med-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 digitranslab/Megamind-v2-VL-med-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for digitranslab/Megamind-v2-VL-med-gguf to start chatting

Pi new

How to use digitranslab/Megamind-v2-VL-med-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf digitranslab/Megamind-v2-VL-med-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": "digitranslab/Megamind-v2-VL-med-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use digitranslab/Megamind-v2-VL-med-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 digitranslab/Megamind-v2-VL-med-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 digitranslab/Megamind-v2-VL-med-gguf:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use digitranslab/Megamind-v2-VL-med-gguf with Docker Model Runner:
```
docker model run hf.co/digitranslab/Megamind-v2-VL-med-gguf:Q4_K_M
```

Lemonade

How to use digitranslab/Megamind-v2-VL-med-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull digitranslab/Megamind-v2-VL-med-gguf:Q4_K_M

Run and chat with the model

lemonade run user.Megamind-v2-VL-med-gguf-Q4_K_M

List all available models

lemonade list

Megamind-v2-VL: Multimodal Agent for Long-Horizon Tasks

Overview

Megamind-v2-VL is an 8B-parameter vision–language model for long-horizon, multi-step tasks in real software environments (e.g., browsers and desktop apps). It combines language reasoning with visual perception to follow complex instructions, maintain intermediate state, and recover from minor execution errors.

We recognize the importance of long-horizon execution for real-world tasks, where small per-step gains compound into much longer successful chains—so Megamind-v2-VL is built for stable, many-step execution. For evaluation, we use The Illusion of Diminishing Returns: Measuring Long-Horizon Execution in LLMs, which measures execution length. This benchmark aligns with public consensus on what makes a strong coding model—steady, low-drift step execution—suggesting that robust long-horizon ability closely tracks better user experience.

Variants

Megamind-v2-VL-low — efficiency-oriented, lower latency
Megamind-v2-VL-med — balanced latency/quality
Megamind-v2-VL-high — deeper reasoning; higher think time

Intended Use

Tasks where the plan and/or knowledge can be provided up front, and success hinges on stable, many-step execution with minimal drift:

Agentic automation & UI control: Stepwise operation in browsers/desktop apps with screenshot grounding and tool calls (e.g., BrowserMCP).

Model Performance

Compared with its base (Qwen-3-VL-8B-Thinking), Megamind-v2-VL shows no degradation on standard text-only and vision tasks—and is slightly better on several—while delivering stronger long-horizon execution on the Illusion of Diminishing Returns benchmark.

Local Deployment

Integration with Megamind

Megamind-v2-VL is optimized for direct integration with the Megamind. Simply select the model from the Megamind interface for immediate access to its full capabilities.

Local Deployment

Using vLLM:

vllm serve digitranslab/Megamind-v2-VL-high \
    --host 0.0.0.0 \
    --port 1234 \
    --enable-auto-tool-choice \
    --tool-call-parser hermes \
    --reasoning-parser qwen3

Using llama.cpp:

llama-server --model Megamind-v2-VL-high-Q8_0.gguf \
    --vision-model-path mmproj-Megamind-v2-VL-high.gguf \
    --host 0.0.0.0 \
    --port 1234 \
    --jinja \
    --no-context-shift

Recommended Parameters

For optimal performance in agentic and general tasks, we recommend the following inference parameters:

temperature: 1.0
top_p: 0.95
top_k: 20
repetition_penalty: 1.0
presence_penalty: 1.5

🤝 Community & Support

Discussions: Hugging Face Community
Megamind: Learn more about the Megamind at megamind.ai

📄 Citation

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GGUF

Model size

8B params

Architecture

qwen3vl

Hardware compatibility

3-bit

4-bit

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8-bit

View +1 variant

Model tree for digitranslab/Megamind-v2-VL-med-gguf

Base model

Qwen/Qwen3-VL-8B-Thinking

Finetuned

digitranslab/Megamind-v2-VL-med

Quantized

(5)

this model

Paper for digitranslab/Megamind-v2-VL-med-gguf

The Illusion of Diminishing Returns: Measuring Long Horizon Execution in LLMs

Paper • 2509.09677 • Published Sep 11, 2025 • 37