This repository contains a collection of Jupyter notebooks and examples for fine-tuning various AI models for different tasks. Each directory focuses on a specific model type or application area.
Finetuning_Repo/
├── Doc_Retrieval_finetuning/ # Fine-tuning sentence transformers for document retrieval
├── FLUX_image_generation/ # Fine-tuning FLUX models for image generation
├── LLM_finetuning/ # Fine-tuning Language Models
├── OCR_finetuning/ # OCR model fine-tuning
│ ├── Image_trascription/ # Fine-tuning for image-to-text transcription (TrOCR)
│ └── Text_detection/ # Fine-tuning for text detection in images (YOLO)
├── RAG_Reranker_finetuning/ # Fine-tuning rerankers for RAG systems
└── VLM_finetuning/ # Fine-tuning Vision Language Models
Contains notebooks for fine-tuning sentence transformer models for document retrieval tasks. Improves embedding quality for specific domains.
Notebooks and scripts for fine-tuning FLUX models for conditional image generation tasks. Enables creating customized image outputs based on specific prompts or conditions.
Resources for fine-tuning Large Language Models for various text generation tasks.
Contains two sub-modules:
- Image_trascription: Fine-tune TrOCR models for converting image text to typed text
- Text_detection: Fine-tune YOLO models for detecting text regions in images
Notebooks for fine-tuning reranker models used in Retrieval-Augmented Generation (RAG) pipelines.
Resources for fine-tuning Vision-Language Models for multimodal tasks.
Each directory contains specific notebooks with detailed instructions. Navigate to the directory of interest and refer to its README and Jupyter notebooks for task-specific guidance.
Each notebook specifies its own requirements, but common dependencies include:
- Python 3.x
- PyTorch
- Transformers
- Datasets
- Specific model libraries (like sentence-transformers, YOLO, etc.)
For detailed requirements, see the specific notebook you wish to run.