This repository is dedicated to experimenting with various Large Language Model (LLM) applications, covering a broad spectrum of techniques and use cases. It includes:
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Fine-Tuning
Detailed notebooks on adapting pre-trained models for specific tasks. These notebooks explore how different hyperparameters, data preprocessing methods, and training routines can influence outcomes. -
Retrieval-Augmented Generation (RAG)
Demonstrations of how to integrate external knowledge sources into LLMs to improve factual consistency and domain relevance. -
AI Agents & Model Orchestration
Initial explorations into chaining or coordinating multiple models for complex task execution, integrating both reasoning and action-based components. -
Inference-Only Testing
Examples of evaluating off-the-shelf models for performance benchmarks, without additional training.
The main goal is to investigate the practical capabilities and limitations of LLMs across diverse scenarios. By combining empirical experiments with methodical documentation, this repository aims to provide insights into effective strategies for deploying, fine-tuning, and extending these models.
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Setup & Dependencies
Instructions and environment configurations to replicate experiments. -
Notebooks
Step-by-step notebooks illustrating the problem setting, methodology, results, and observations. -
Results & Observations
Metrics, logs, and brief analyses of model performance on various tasks.
Feedback, suggestions, and pull requests are welcome. Whether it’s proposing a novel approach, pointing out issues, or sharing your own experience with similar models, your contributions enrich the collective learning process.
Thank you for visiting. This repository will evolve as new methods, tasks, and models emerge, and it reflects a continuous effort to understand and optimize the capabilities of modern language models in real-world scenarios.