#PEFT

Parameter-Efficient Fine-Tuning (PEFT) offers a cost-effective way to adapt large pretrained models with reduced computational and storage needs, maintaining high performance similar to fully fine-tuned models. PEFT works with tools like Transformers, Diffusers, and Accelerate, making it versatile for model training and inference across different domains. This method helps in managing large models on consumer hardware by minimizing memory consumption without compromising accuracy.

This collection offers detailed interview notes for Large Language Models (LLMs) derived from expert experiences. It includes foundational to advanced preparation, addressing frequent interview questions, model structures, and training goals. The guide provides strategies for managing issues such as repetitive outputs and model choice in different fields, as well as insights on distributed training, efficient tuning, and inference. It serves as a practical resource for understanding LLMs in professional interviews without excessive embellishment.

Explore a methodology to enhance the performance of LLaMa3-8B in multiple non-English languages through advanced fine-tuning techniques and Retrieval-Augmented Generation (RAG). This guide details the step-by-step process, from dataset translation to the use of QLoRA and PEFT for efficient language model tuning. It covers a variety of foundation models, including LLaMa3 and Mistral, providing broad compatibility. Notably cost-effective, the project can be executed using free GPU resources like Google Colab. Discover the integration of various translation paradigms and implementation of DPO for improved model responses, suitable for developers enhancing multilingual chat platforms.

Discover a framework designed for efficient fine-tuning of advanced large language models via a variety of adapter techniques. This framework, supporting models such as LLaMa, OPT, BLOOM, and GPT-J, enables parameter-efficient learning across multiple tasks. It offers compatibility with adapter methods like LoRA, AdapterH, and Parallel adapters, enhancing the performance of NLP applications. Keep informed about the latest achievements, including surpassing benchmarks such as ChatGPT in commonsense and mathematics evaluations.

Discover ReLoRA, a methodology that enhances neural network pretraining by employing low-rank updates to improve training efficiency. This technology provides adaptable configurations with reset frequency control and effective optimizer state management, ideal for large-scale neural models. Fully customizable in terms of batch sizes and learning rates, it supports distributed training via PyTorch DDP and ensures practical implementation for AI research advancements, offering enhanced performance and reproducibility from pre-trained models.

The project implements advanced fine-tuning techniques for the ChatGLM-6B model, including LoRA, P-Tuning V2, and Reinforcement Learning with Human Feedback (RLHF). It features a comprehensive Web UI for single GPU-based training, evaluation, and inference, highlighting its role in optimizing large language models. The repository supports various datasets like Stanford Alpaca, BELLE, and GPT-4 generated data, enhancing ChatGLM's adaptability to diverse datasets and tuning methods. Although the project is no longer actively maintained, it has significantly contributed to the efficient tuning of language models.