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- # Gemma Fine-tuning UI
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-
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- A user-friendly interface for fine-tuning Google's Gemma models using Unsloth optimizations.
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-
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- ## Features
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-
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- - Easy-to-use web interface for model fine-tuning
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- - Support for multiple data formats (CSV, JSONL, TEXT)
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- - Parameter-efficient fine-tuning with LoRA
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- - Real-time training progress visualization
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- - Model export in multiple formats
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- - Integrated text generation testing
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-
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- ## Installation
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-
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- ```bash
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- git clone https://github.com/codewithdark-git/Gemma-Finetune.git
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- cd Gemma-Finetune
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- pip install -r requirements.txt
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- ```
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-
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- ## Usage
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-
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- 1. Run the application:
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- ```bash
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- python main.py
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- ```
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-
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- 2. Follow the UI steps:
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- - Upload your dataset
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- - Configure model parameters
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- - Start training
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- - Test and export your model
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-
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- ## Requirements
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-
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- See requirements.txt for detailed dependencies.
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-
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- ## License
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-
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- MIT License
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ license: mit
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+ title: Gemma FineTuner
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+ sdk: gradio
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+ emoji: 🔥
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+ colorFrom: gray
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+ colorTo: green
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+ pinned: true
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+ ---
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+ # Gemma Fine-tuning UI
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+
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+ A user-friendly interface for fine-tuning Google's Gemma models using Unsloth optimizations.
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+
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+ ## Features
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+
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+ - Easy-to-use web interface for model fine-tuning
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+ - Support for multiple data formats (CSV, JSONL, TEXT)
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+ - Parameter-efficient fine-tuning with LoRA
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+ - Real-time training progress visualization
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+ - Model export in multiple formats
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+ - Integrated text generation testing
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+
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+ ## Installation
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+
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+ ```bash
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+ git clone https://github.com/codewithdark-git/Gemma-Finetune.git
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+ cd Gemma-Finetune
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+ pip install -r requirements.txt
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+ ```
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+
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+ ## Usage
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+
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+ 1. Run the application:
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+ ```bash
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+ python main.py
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+ ```
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+
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+ 2. Follow the UI steps:
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+ - Upload your dataset
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+ - Configure model parameters
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+ - Start training
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+ - Test and export your model
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+
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+ ## Requirements
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+
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+ See requirements.txt for detailed dependencies.
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+
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+ ## License
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+
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+ MIT License