koboshchan/genshinPlayData
Updated • 3
How to use koboshchan/Lumine-Agent-Pretrain-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-Pretrain-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-Pretrain-VL-7B")
model = AutoModelForMultimodalLM.from_pretrained("koboshchan/Lumine-Agent-Pretrain-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]:]))How to use koboshchan/Lumine-Agent-Pretrain-VL-7B with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "koboshchan/Lumine-Agent-Pretrain-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-Pretrain-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 run hf.co/koboshchan/Lumine-Agent-Pretrain-VL-7B
How to use koboshchan/Lumine-Agent-Pretrain-VL-7B with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "koboshchan/Lumine-Agent-Pretrain-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-Pretrain-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 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-Pretrain-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-Pretrain-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"
}
}
]
}
]
}'How to use koboshchan/Lumine-Agent-Pretrain-VL-7B with Docker Model Runner:
docker model run hf.co/koboshchan/Lumine-Agent-Pretrain-VL-7B
Step 1: pretrain
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},
}
Base model
Qwen/Qwen2-VL-7B