Instructions to use Zhiqiang007/Math-LLaVA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Zhiqiang007/Math-LLaVA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="Zhiqiang007/Math-LLaVA")# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Zhiqiang007/Math-LLaVA", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Zhiqiang007/Math-LLaVA with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Zhiqiang007/Math-LLaVA" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Zhiqiang007/Math-LLaVA", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Zhiqiang007/Math-LLaVA
- SGLang
How to use Zhiqiang007/Math-LLaVA 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 "Zhiqiang007/Math-LLaVA" \ --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": "Zhiqiang007/Math-LLaVA", "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 "Zhiqiang007/Math-LLaVA" \ --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": "Zhiqiang007/Math-LLaVA", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Zhiqiang007/Math-LLaVA with Docker Model Runner:
docker model run hf.co/Zhiqiang007/Math-LLaVA
Math-LLaVA-13B Model Card
Model details
Model type: Math-LLaVA is an open-source MLLM by fine-tuning LLaVA-1.5-13B on selected and GPT4-Vision-assisted synthesized MathV360K data.
Model date: Math-LLaVA-13B was trained in June 2024.
Paper or resources for more information: [Paper] [Code]
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 Math-LLaVA is research on multimodal large language models, multimodal reasoning and question answering.
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
- MathV360K instruction-tuning data
Evaluation dataset
A collection of 3 benchmarks, including 2 multimodal mathematical reasoning benchmarks and 1 benchmark for multi-discipline multimodal reasoning.
- Downloads last month
- 21