lxtung95/

lyricloop-llm

- Fine-Tuning: Implemented Low-Rank Adaptation (LoRA) to specialize the model in rhythmic patterns while preserving base reasoning.
- Optimization: Used 4-bit Quantization (QLoRA) via bitsandbytes to reduce the memory footprint during training.
- Instruction Tuning: Supervised Fine-Tuning (SFT) with custom templates to enforce structural and genre constraints.

- app.py: main streamlit application entry point and UI logic.
- src/lyricloop/: core modular package containing engine logic:
    - config.py: global constants and path management.
    - data.py: prompt engineering and dataset preprocessing.
    - environment.py: hardware-aware setup (MPS/CPU/CUDA).
    - metrics.py: inference execution and perplexity scoring.
    - viz.py: standardized plotting and visual utilities.
- notebooks/: development playground, training workflows, and EDA.
- reports/: written technical documentation and project summaries.
- assets/: visual artifacts and plots used in documentation.
- requirements.txt: dependency management for environment parity.

- Corpus: 5mm+ Song Lyrics (Genius Dataset).
- Metadata: Artist mapping via Pitchfork Reviews.
- Stack: Python, Hugging Face (Transformers, PEFT, TRL), PyTorch, and Google Colab (L4 GPU).

- Full Project Workspace (Google Drive): [Access the Notebooks & Raw Data](https://drive.google.com/drive/folders/1M5SJRaaK8OaskUgEsBupgGVN_-fQS3i4?usp=sharing)
- Training Environment: Google Colab (L4 GPU)

- Run on Hugging Face lxtung95/lyricloop
- App URL: https://lxtung95-lyricloop.hf.space/
    1. Details: Enter a song title and an Artist Aesthetic (e.g., Taylor Swift) to set the tone.
    2. Genre: Select your target genre to adjust rhythmic density.
    3. Compose: Use the Creativity (Temperature) slider to control experimental word choice.
    4. Export: Download the final composition as a .txt file for your creative workflow.

Visit my GitHub repository for the latest scripts and downloads:
https://github.com/lxntung95