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

Libraries

How to use digitranslab/Megamind-v2-VL-high 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-high")
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 AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("digitranslab/Megamind-v2-VL-high")
model = AutoModelForImageTextToText.from_pretrained("digitranslab/Megamind-v2-VL-high")
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?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use digitranslab/Megamind-v2-VL-high 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-high"
# 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-high",
		"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-high

SGLang

How to use digitranslab/Megamind-v2-VL-high 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-high" \
    --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-high",
		"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-high" \
        --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-high",
		"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"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use digitranslab/Megamind-v2-VL-high with Docker Model Runner:
```
docker model run hf.co/digitranslab/Megamind-v2-VL-high
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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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Safetensors

Model size

9B params

Tensor type

BF16

Model tree for digitranslab/Megamind-v2-VL-high

Base model

Qwen/Qwen3-VL-8B-Thinking

Finetuned

(60)

this model

Finetunes

1 model

Quantizations

5 models

Paper for digitranslab/Megamind-v2-VL-high

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

Paper • 2509.09677 • Published Sep 11, 2025 • 37