Welcome to the LLM Research Toolbox! This repository serves as a central hub to explore, organize, and navigate multiple specialized repositories dedicated to advancing the field of Large Language Models (LLMs). Each repository focuses on a specific aspect of LLM development, from fine-tuning techniques to evaluation and multimodal capabilities.
The field of LLMs is vast, and breaking down the components into separate, modular repositories allows for better focus and collaboration. This central README helps to easily discover and access these resources.
Exploring instruction tuning and supervised fine-tuning for language models with chat templates and task-specific dataset adaptations.
Key Highlights:
- Training and fine-tuning.
- Chat template integration.
- Task-specific dataset preparation.
Innovative methods like DPO and ORPO for aligning language models with human preferences efficiently and effectively.
Key Highlights:
- DPO (Direct Preference Optimization) and ORPO methodologies.
- Emphasis on fine-tuning aligned with human preferences.
Efficiently adapt large language models to tasks with PEFT methods like LoRA and prompt tuning, minimizing memory and compute requirements.
Key Highlights:
- LoRA (Low-Rank Adaptation).
- Memory-efficient PEFT methods.
Evaluate and compare language models using lighteval for benchmarks and custom tasks, with tools for flexible, efficient analysis.
Key Highlights:
- Flexible evaluation tools.
- Benchmarking with lighteval.
- Custom task support.
Explore Vision Language Models (VLMs) for multimodal tasks like image captioning, VQA, and fine-tuning for domain-specific or human-aligned applications.
Key Highlights:
- Multimodal applications (e.g., image captioning, VQA).
- Fine-tuning for domain-specific tasks.
Generate synthetic datasets for instruction tuning and preference alignment using tools like distilabel for efficient and scalable data creation.
Key Highlights:
- Efficient synthetic data generation.
- Applications for instruction tuning and preference alignment.