Instructions to use madokalif/minecraft-vla-stage1-dit-groot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use madokalif/minecraft-vla-stage1-dit-groot with Transformers:

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

pipe = pipeline("image-text-to-text", model="madokalif/minecraft-vla-stage1-dit-groot")
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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("madokalif/minecraft-vla-stage1-dit-groot")
model = AutoModelForMultimodalLM.from_pretrained("madokalif/minecraft-vla-stage1-dit-groot", device_map="auto")
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 Settings

vLLM

How to use madokalif/minecraft-vla-stage1-dit-groot with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "madokalif/minecraft-vla-stage1-dit-groot"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "madokalif/minecraft-vla-stage1-dit-groot",
		"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/madokalif/minecraft-vla-stage1-dit-groot

SGLang

How to use madokalif/minecraft-vla-stage1-dit-groot 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 "madokalif/minecraft-vla-stage1-dit-groot" \
    --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": "madokalif/minecraft-vla-stage1-dit-groot",
		"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 "madokalif/minecraft-vla-stage1-dit-groot" \
        --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": "madokalif/minecraft-vla-stage1-dit-groot",
		"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 madokalif/minecraft-vla-stage1-dit-groot with Docker Model Runner:
```
docker model run hf.co/madokalif/minecraft-vla-stage1-dit-groot
```

minecraft-vla-stage1-dit-groot

Stage-1 checkpoint from a Minecraft vision-language-action experiment (groot action head). Archived here so the local copy could be freed; no benchmark numbers are claimed.

Backbone: Qwen2-VL-7B (qwen2_vl, 28 layers, hidden 3584), bf16, trained from an internal Minecraft-adapted base (mc-base-qwen2-vl-7b-2502).
expert.pt (2.4 GB) is the DiT action expert; it is not loaded by AutoModel.from_pretrained and needs the original training code.
Total ~18 GB: 4 safetensors shards + tokenizer/processor config + the expert.

License is inherited from the Qwen2-VL backbone (Apache-2.0). The Minecraft training data is not included.

Downloads last month: 16

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for madokalif/minecraft-vla-stage1-dit-groot

Base model

Qwen/Qwen2-VL-7B

Finetuned

Qwen/Qwen2-VL-7B-Instruct

Finetuned

(607)

this model