Instructions to use BELLE-2/BELLE-VL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BELLE-2/BELLE-VL with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="BELLE-2/BELLE-VL", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("BELLE-2/BELLE-VL", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use BELLE-2/BELLE-VL with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "BELLE-2/BELLE-VL" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "BELLE-2/BELLE-VL", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/BELLE-2/BELLE-VL
- SGLang
How to use BELLE-2/BELLE-VL 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 "BELLE-2/BELLE-VL" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "BELLE-2/BELLE-VL", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "BELLE-2/BELLE-VL" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "BELLE-2/BELLE-VL", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use BELLE-2/BELLE-VL with Docker Model Runner:
docker model run hf.co/BELLE-2/BELLE-VL
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README.md
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###
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[MME](https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models/tree/Evaluation)是一个针对多模态大型语言模型的全面评估基准。它在总共14个子任务上测量感知和认知能力,包括
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包括存在性、计数、位置、颜色、海报、名人、场景、地标、艺术作品、OCR、常识推理、数值计算、文本翻译和代码推理等。BELLE-VL在感知评测共获得1595.34分,超过LLava和Qwen-VL.详情如下:
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### MME Benchmark
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[MME](https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models/tree/Evaluation)是一个针对多模态大型语言模型的全面评估基准。它在总共14个子任务上测量感知和认知能力,包括
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包括存在性、计数、位置、颜色、海报、名人、场景、地标、艺术作品、OCR、常识推理、数值计算、文本翻译和代码推理等。BELLE-VL在感知评测共获得1595.34分,超过LLava和Qwen-VL.详情如下:
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