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https://github.com/bhimrazy/chat-with-phi-3-vision
Chat with Phi-3-vision-128k-instruct
https://github.com/bhimrazy/chat-with-phi-3-vision
chat-application huggingface litserve phi-3-vision streamlit
Last synced: about 18 hours ago
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Chat with Phi-3-vision-128k-instruct
- Host: GitHub
- URL: https://github.com/bhimrazy/chat-with-phi-3-vision
- Owner: bhimrazy
- License: mit
- Created: 2024-05-23T08:30:21.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2024-05-23T10:01:09.000Z (8 months ago)
- Last Synced: 2024-05-23T10:25:04.422Z (8 months ago)
- Topics: chat-application, huggingface, litserve, phi-3-vision, streamlit
- Language: Jupyter Notebook
- Homepage:
- Size: 892 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
Chat with Phi 3.5/3 Vision LLMs
## Overview
[**Phi-3.5-vision**](https://huggingface.co/microsoft/Phi-3.5-vision-instruct) is a lightweight, state-of-the-art open multimodal model built upon datasets which include - synthetic data and filtered publicly available websites - with a focus on very high-quality, reasoning dense data both on text and vision.
This model enables multi-frame image understanding, image comparison, multi-image summarization/storytelling, and video summarization, which have broad applications in office scenarios.
## Getting Started
Follow these steps to set up and run the project:
### 1. Install Dependencies
i. **Download and Install NVIDIA CUDA**
Visit the [NVIDIA CUDA Toolkit Downloads page](https://developer.nvidia.com/cuda-downloads) and follow the instructions to install CUDA compatible with your system.
ii. **Install Required Python Packages**
Ensure you have all the necessary dependencies installed by running the following commands:
```bash
pip install -r requirements.txt
pip install flash_attn
```
If you encounter any issues while installing `flash_attn`, refer to the [FlashAttention Installation Guide](https://github.com/Dao-AILab/flash-attention?tab=readme-ov-file#installation-and-features) for troubleshooting tips and additional setup details.
### 2. Start the API Server
Launch the API server powered by [LitServe](https://github.com/Lightning-AI/LitServe):
```bash
python server.py
```
### 3. Launch the Streamlit App
Start the Streamlit application with the following command:
```bash
streamlit run app.py
```
## About
This project is developed and maintained with ❤️ by [Bhimraj Yadav](https://github.com/bhimrazy).