Update README.md

README.md

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 license: mit
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+Multi-Modal LLM Demo with Flan-T5
+This project is a multi-modal language model application that accepts text, image, and audio inputs to generate a text response. It leverages:
+
+CLIP from OpenAI for image embeddings.
+
+Whisper from OpenAI for audio transcription.
+
+Flan-T5 Large from Google as an instruction‑tuned text generation model.
+
+Gradio to build an interactive web interface.
+
+Hugging Face Spaces for deployment.
+
+The goal is to demonstrate how different modalities can be fused into a single prompt to produce coherent text output.
+
+Features
+Multi-Modal Inputs:
+
+Text: Users can type in their queries.
+
+Image: Users can upload images; the app processes these using CLIP.
+
+Audio: Users can upload audio files; the app transcribes them using Whisper.
+
+Instruction-Tuned Text Generation:
+
+Uses Flan‑T5 Large to generate responses based on the fused prompt.
+
+Customizable Decoding:
+
+Advanced generation parameters such as temperature, top_p, and repetition_penalty are applied to produce varied and coherent outputs.
+
+Interactive UI:
+
+A clean, ChatGPT-like interface built with Gradio.
+
+Installation & Setup
+Requirements
+Ensure your environment has the following dependencies. You can install them via the provided requirements.txt:
+
+txt
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+torch
+transformers>=4.31.0
+accelerate>=0.20.0
+gradio
+soundfile
+Getting Started
+Clone the Repository:
+
+bash
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+git clone <your-repo-url>
+cd <your-repo-directory>
+(Optional) Create a Virtual Environment:
+
+bash
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+python -m venv env
+source env/bin/activate  # On Windows: env\Scripts\activate
+Install Dependencies:
+
+bash
+Copy
+pip install --upgrade pip
+pip install -r requirements.txt
+Running the App Locally
+The main application is defined in app.py. To run the app locally:
+
+bash
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+python app.py
+This will launch the Gradio interface locally. Open the URL provided in your terminal to interact with the app via your browser.
+
+Project Structure
+Copy
+├── app.py
+├── requirements.txt
+└── README.md
+app.py:
+Contains the complete code for processing multi-modal inputs and generating responses.
+
+requirements.txt:
+Lists all the required dependencies.
+
+README.md:
+Provides an overview, installation instructions, and usage details for the project.
+
+How It Works
+Image Processing:
+
+The app uses the CLIP model to extract image embeddings.
+
+A linear projection layer converts these 512-dimensional embeddings to the 768-dimensional space expected by Flan‑T5.
+
+Audio Processing:
+
+Whisper transcribes audio files into text.
+
+The transcription is appended to the text prompt.
+
+Text Processing:
+
+The provided text input (if any) is combined with placeholders representing the image and audio content.
+
+The fused prompt is tokenized and fed into the Flan‑T5 model to generate a response.
+
+Decoding:
+
+Advanced generation parameters such as temperature, top_p, repetition_penalty, and do_sample are applied to guide the text generation process, ensuring varied and coherent outputs.
+
+Deployment:
+
+The Gradio interface provides an intuitive, web-based UI.
+
+The app is designed to be deployed on Hugging Face Spaces, making it easily accessible.
+
+
+Future Improvements
+Fine-Tuning:
+
+Fine-tune the projection layers and the text model on a dedicated multi-modal dataset (e.g., Instruct 150k) using techniques like QLoRa.
+
+Enhanced Fusion:
+
+Develop more sophisticated fusion strategies beyond concatenating placeholder tags.
+
+Model Upgrades:
+
+Experiment with different instruction-tuned or conversation-focused models to improve the quality of generated responses.
+
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference