Instructions to use koboshchan/Lumine-Agent-Pretraining-VL-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use koboshchan/Lumine-Agent-Pretraining-VL-7B with Transformers:

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

pipe = pipeline("image-text-to-text", model="koboshchan/Lumine-Agent-Pretraining-VL-7B")
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("koboshchan/Lumine-Agent-Pretraining-VL-7B")
model = AutoModelForMultimodalLM.from_pretrained("koboshchan/Lumine-Agent-Pretraining-VL-7B")
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 koboshchan/Lumine-Agent-Pretraining-VL-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "koboshchan/Lumine-Agent-Pretraining-VL-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "koboshchan/Lumine-Agent-Pretraining-VL-7B",
		"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/koboshchan/Lumine-Agent-Pretraining-VL-7B

SGLang

How to use koboshchan/Lumine-Agent-Pretraining-VL-7B 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 "koboshchan/Lumine-Agent-Pretraining-VL-7B" \
    --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": "koboshchan/Lumine-Agent-Pretraining-VL-7B",
		"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 "koboshchan/Lumine-Agent-Pretraining-VL-7B" \
        --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": "koboshchan/Lumine-Agent-Pretraining-VL-7B",
		"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 koboshchan/Lumine-Agent-Pretraining-VL-7B with Docker Model Runner:
```
docker model run hf.co/koboshchan/Lumine-Agent-Pretraining-VL-7B
```

Lumine Agent

WARNING DO NOT USE. OLD VERSION, MISSING CUSTOM TOKENS.

USE koboshchan/Lumine-Agent-Pretrain-VL-7B INSTEAD

Trained on 30 minutes of game play. Based on Qwen2-VL-7B

https://github.com/zlc1004/Lumine

@misc{tan2025lumineopenrecipebuilding,
      title={Lumine: An Open Recipe for Building Generalist Agents in 3D Open Worlds}, 
      author={Weihao Tan and Xiangyang Li and Yunhao Fang and Heyuan Yao and Shi Yan and Hao Luo and Tenglong Ao and Huihui Li and Hongbin Ren and Bairen Yi and Yujia Qin and Bo An and Libin Liu and Guang Shi},
      year={2025},
      eprint={2511.08892},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2511.08892}, 
}

Downloads last month: 10

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for koboshchan/Lumine-Agent-Pretraining-VL-7B

Base model

Qwen/Qwen2-VL-7B

Finetuned

(23)

this model

Dataset used to train koboshchan/Lumine-Agent-Pretraining-VL-7B

Paper for koboshchan/Lumine-Agent-Pretraining-VL-7B

Lumine: An Open Recipe for Building Generalist Agents in 3D Open Worlds

Paper • 2511.08892 • Published Nov 12, 2025 • 218