Instructions to use ShareGPTVideo/LLaVA-Hound-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ShareGPTVideo/LLaVA-Hound-SFT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="ShareGPTVideo/LLaVA-Hound-SFT")# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("ShareGPTVideo/LLaVA-Hound-SFT", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use ShareGPTVideo/LLaVA-Hound-SFT with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ShareGPTVideo/LLaVA-Hound-SFT" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ShareGPTVideo/LLaVA-Hound-SFT", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ShareGPTVideo/LLaVA-Hound-SFT
- SGLang
How to use ShareGPTVideo/LLaVA-Hound-SFT 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 "ShareGPTVideo/LLaVA-Hound-SFT" \ --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": "ShareGPTVideo/LLaVA-Hound-SFT", "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 "ShareGPTVideo/LLaVA-Hound-SFT" \ --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": "ShareGPTVideo/LLaVA-Hound-SFT", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ShareGPTVideo/LLaVA-Hound-SFT with Docker Model Runner:
docker model run hf.co/ShareGPTVideo/LLaVA-Hound-SFT
LLaVA-Hound Model Card
Model details
Model type: LLaVA-Hound is an open-source video large multimodal model, fine-tuned from video instruction following data based on large language model.
This model is the SFT version on image and video instruction dataset trained from ShareGPTVideo/LLaVA-Hound-Pretrain.
Base LLM: lmsys/vicuna-7b-v1.5
Model date: Trained on March 15, 2024.
Paper or resources for more information:
Paper: https://huggingface.co/papers/2404.01258
Code: https://github.com/RifleZhang/LLaVA-Hound-DPO
License
lmsys/vicuna-7b-v1.5 license.
Where to send questions or comments about the model: https://github.com/RifleZhang/LLaVA-Hound-DPO/issues
Intended use
Primary intended uses: Video (image) instruction-following.
Primary intended users: Researchers in artificial intelligence, large multimodal model, etc.
Training dataset
ShareGPTVideo dataset.
Evaluation
Follow https://github.com/RifleZhang/LLaVA-Hound-DPO/blob/main/README.md
Paper
https://huggingface.co/papers/2404.01258
citation
@article{zhang2024direct,
title={Direct Preference Optimization of Video Large Multimodal Models from Language Model Reward},
author={Zhang, Ruohong and Gui, Liangke and Sun, Zhiqing and Feng, Yihao and Xu, Keyang and Zhang, Yuanhan and Fu, Di and Li, Chunyuan and Hauptmann, Alexander and Bisk, Yonatan and others},
journal={arXiv preprint arXiv:2404.01258},
year={2024}
}
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