Instructions to use Zery/MV-LLaVA-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Zery/MV-LLaVA-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="Zery/MV-LLaVA-7B")# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Zery/MV-LLaVA-7B", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Zery/MV-LLaVA-7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Zery/MV-LLaVA-7B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Zery/MV-LLaVA-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Zery/MV-LLaVA-7B
- SGLang
How to use Zery/MV-LLaVA-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 "Zery/MV-LLaVA-7B" \ --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": "Zery/MV-LLaVA-7B", "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 "Zery/MV-LLaVA-7B" \ --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": "Zery/MV-LLaVA-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Zery/MV-LLaVA-7B with Docker Model Runner:
docker model run hf.co/Zery/MV-LLaVA-7B
MV-LLaVA-7B Model Card
Model details
Model type: MV-LLaVA-7B is an open-source chatbot for 3D multi-view images trained by fine-tuning CLIP vision tower and LLaMA/Vicuna on GPT4-Vision-assisted BS-Objaverse data and ShareGPT4V data.
Model date: MV-LLaVA-7B was trained in Apr, 2024.
Paper or resources for more information: [Project] [Paper] [Code]
Usage
You can directly utilize this model as we provide in our [repository].
License
Llama 2 is licensed under the LLAMA 2 Community License, Copyright (c) Meta Platforms, Inc. All Rights Reserved.
Intended use
Primary intended uses: The primary use of ShareGPT4V-7B is research on large multimodal models and chatbots for 3D content. Primary intended users: The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
Training dataset
- 1.2M ShareGPT4V-PT data
- 30K GPT4-Vision-generated multi-view image-text pairs
- LLaVA instruction-tuning data
- Downloads last month
- 8