Play-with-LLMs - Gain Insights into Training and Evaluating Large Language Models

Play with LLMs

"Play with LLMs" is an exciting project that explores the world of Large Language Models (LLMs). This project provides a practical approach to training and evaluating these powerful models, as well as creating engaging applications using techniques like Retrieval-Augmented Generation (RAG), Agent, and Chain methods.

🚀 Ready to Use Code: Practical Examples

One of the standout features of "Play with LLMs" is its emphasis on providing ready-to-use code. This means the project comes with pre-prepared code snippets and tutorials, ensuring that anyone can get started with LLMs without needing extensive programming skills. Here are some notable examples included in the project:

Mistral-8x7b-Instruct with JSON Stability: A guide on achieving stable JSON output format using Llamacpp grammar.
Mistral-8x7b-Instruct CoT Agent: Demonstrates a "Chain of Thought" agent that reasons step-by-step.
Mistral-8x7b-Instruct ReAct Agent with Tool Call: Covers the implementation of a ReAct agent, which uses additional tools for improved functionality.
Llama3-8b-Instruct Techniques: Provides various methods for interacting with the Llama3-8b model, utilizing transformers, vLLM, and Llamacpp.
Chinese-Llama3-8b Adaptation: Focuses on making Llama3 more proficient in Chinese through DPO fine-tuning.
Model Quantization and Conversion: Details the process of converting and quantizing models to the GGUF format for uploading to platforms like Hugging Face.
Advanced ReAct Applications: Explores more sophisticated applications of the ReAct model.

🐬 Diving Deep into LLMs: Training and Fine-tuning

For those interested in a deeper understanding of LLMs, the project covers essential topics like pretraining, fine-tuning, and Reinforcement Learning from Human Feedback (RLHF). A specific highlight is the use of "qlora-finetune-Baichuan-7B," which provides a detailed guide on fine-tuning using qlora techniques.

Real-World Case Studies

The project includes compelling case studies demonstrating different applications of the technology:

Mixtral 8x7b ReAct: Showcases a simplified ReAct example with visual representation.
Llama3-8b ReAct: Similar to Mixtral, this example offers insights into how ReAct operates within the Llama3 model.

These elements combine to make "Play with LLMs" a comprehensive resource for both newcomers and seasoned practitioners in the field of large language models. The project's emphasis on practical application, combined with easy-to-follow guides, makes it an invaluable tool for anyone looking to explore the potential of LLMs.