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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- 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
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Download README.md from Zhiqiang007/Math-LLaVA: direct link, hf CLI and curl.
- Browser
- Download file 1.22 kB
-
https://huggingface.co/Zhiqiang007/Math-LLaVA/resolve/main/README.md
- Command line
-
hf download hf://Zhiqiang007/Math-LLaVA/README.md
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curl -L -o README.md https://huggingface.co/Zhiqiang007/Math-LLaVA/resolve/main/README.md
1.22 kB
| pipeline_tag: image-text-to-text | |
| <br> | |
| <br> | |
| # 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](https://huggingface.co/datasets/Zhiqiang007/MathV360K/tree/main) data. | |
| **Model date:** | |
| Math-LLaVA-13B was trained in June 2024. | |
| **Paper or resources for more information:** | |
| [[Paper](http://arxiv.org/abs/2406.17294)] [[Code](https://github.com/HZQ950419/Math-LLaVA)] | |
| ## 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. | |