llm-finetuned-model

Finetuning an Open-Source LLM

This project adapts large language models to domain-specific tasks, leveraging parameter-efficient techniques (LoRA/QLoRA), cloud deployment, and workflow orchestration.
This repository contains code for fine-tuning large language models (LLMs) on custom datasets, handling cloud orchestration, and uploading final models to Hugging Face Hub.

Objective

This project aims to fine-tune large language models (LLMs) efficiently for domain-specific tasks, enabling easy deployment via cloud orchestration and interactive demo applications. It demonstrates advanced techniques like parameter-efficient fine-tuning (LoRA/QLoRA) and streamlined workflow automation.

Getting Started

• Clone this repository
• Install Python dependencies
• See demo_app/app.py to launch the demo

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Project Structure

• src/model/: Core model training, evaluation, and upload scripts.
• configs/train_config.yaml: Configuration file for training hyperparameters and paths.
• models/llm-finetuned/: Output directory where trained model checkpoints and tokenizer files are saved.
• upload_model.py: Script to upload saved model files to Hugging Face Hub.
• src/eval/: Evaluation scripts for the trained models.

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Setup Instructions

1. Create and activate your Python environment:

   conda create -n llm-finetuning python=3.10 -y
   conda activate llm-finetuning
   

2. Install required dependencies:

   pip install -r requirements.txt
   

3. Configure configs/train_config.yaml according to your data paths and training parameters.

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Training

Run model training with:

python src/model/train.py

This will:

• Load your dataset from the configured path.
• Fine-tune the specified model.
• Save model checkpoints and tokenizer files in models/llm-finetuned/.

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Uploading Model to Hugging Face

After training completes, upload your model files with:

python upload_model.py

Ensure your upload_model.py points to the correct local folder and Hugging Face repository:

from huggingface_hub import HfApi

api = HfApi() api.upload_folder( repo_id="your-hf-username/your-model-repo", folder_path="models/llm-finetuned", path_in_repo="", repo_type="model" ) print("Upload completed")

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Streamlit Demo Application

This project also includes a Streamlit app for easy demonstration and testing of the fine-tuned model.

Running the Streamlit App

1. Install Streamlit if not installed:

   pip install streamlit
   

2. Run the app:

   streamlit run src/demo_app/app.py
   

3. Open the local URL provided by Streamlit in your browser to interact with the model.

Configuration

• Update API keys or model paths in the app configuration file if needed.
• Modify the app code to customize UI or add features.

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Git Workflow for Updates

To commit and push changes safely when collaborating or syncing with remote:

git add .
git commit -m "Describe your changes"
git pull --rebase

Resolve conflicts if any, then: git push

For stuck rebase issues, clear the rebase state with: For Git Bash or WSL rm -rf .git/rebase-merge

Or in PowerShell Remove-Item -Recurse -Force .git\rebase-merge

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Troubleshooting

• If Git complains about index.lock, delete the lock file:

  rm -f .git/index.lock
  

  Or in PowerShell:

  Remove-Item .git\index.lock
  

• Always commit or stash changes before pulling:

  git add .
  git commit -m "Save progress"
  git pull --rebase
  

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Future Scope

• Expand support for additional LLM architectures and datasets.
• Integrate advanced evaluation metrics and error analysis tools.
• Develop fully featured web applications for user-friendly model interaction.
• Optimize cloud deployment pipelines for scalable inference.
• Implement autoML capabilities for hyperparameter and architecture tuning.
• Add multilingual and multimodal fine-tuning workflows.

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Contact

For questions or issues, open an issue in this repository or reach out via email: [email protected]

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