zyds/transformers-code
Transformers Code
This project is a collection of scripts and workflows for training, fine-tuning, and deploying large language models using the Hugging Face Transformers toolkit. It functions as a distributed training framework, a library for natural language processing task implementations, and a system for building retrieval-augmented generation chatbots.
The repository includes specialized tools for model optimization, such as a Bayesian hyperparameter optimizer for automatically tuning model settings. It provides implementations for scaling model training across multiple graphics processors using data parallelism and low-precision quantization.
The library covers a wide range of natural language processing capabilities, including text summarization, question answering, token classification, and sentence similarity measurement. It also supports the development of generative and retrieval-based conversational agents.
The project is implemented primarily using Jupyter Notebooks.
Features
- Large Language Model Training Frameworks - Provides a comprehensive framework for training and optimizing large language models across multi-GPU environments.
- Language Model Training - Provides tools and techniques for training large language models to generate coherent and contextually relevant text.
- Knowledge Base Retrieval - Supports generating accurate responses by retrieving relevant information from an external knowledge base.
- Retrieval-Augmented Generation - Builds conversational agents using retrieval-augmented generation to integrate external knowledge bases.
- Distributed Training Frameworks - Ships a distributed training framework to scale LLM workloads using data parallelism and quantization.
- Data-Parallel Training - Implements distributed data-parallel training to scale workloads across multiple graphics processors.
- Distributed Training - Provides a framework for distributing model training across multiple hardware accelerators.
- Conversational Response Generation - Enables the generation of fluid, human-like conversational responses using prompt and interaction history.
- Mixed Precision Training - Utilizes mixed-precision training and low-precision formats to reduce memory and increase throughput.
- Mixed-Precision Quantization - Employs mixed-precision quantization to reduce memory usage and accelerate training speed.
- Multi-GPU Training Utilities - Enables scaling of model training across multiple GPUs using parallel data strategies.
- Token Classification - Identifies and categorizes individual words or phrases within a text into predefined labels.
- Natural Language Processing - Executes a wide range of NLP tasks including summarization and sentence similarity measurement.
- Text Summarization - Condenses long documents into shorter versions while preserving core meaning and essential information.
- Parameter Efficient Fine-Tuning - Supports parameter-efficient fine-tuning to adapt large models with minimal trainable parameters.
- Sequence-to-Sequence Transformer Architectures - Utilizes transformer-based sequence modeling and self-attention mechanisms to process text sequences.
- Transformer-Based NLP Libraries - Provides a library of pre-configured transformer-based pipelines for various NLP task implementations.
- Transformer Training Toolkits - Provides a specialized toolkit for the full lifecycle of transformer model training and deployment.
- Question Answering - Enables extracting or generating specific answers from a given text based on user queries.
- AI Chatbots - Develops generative AI chatbots that produce human-like responses based on interaction histories.
- Hyperparameter Optimization Tools - Includes a Bayesian optimization tool for automatically tuning language model training parameters.
- Hyperparameter Optimization - Implements automated hyperparameter optimization using Bayesian frameworks to maximize model performance.
- Bayesian Optimization - Provides a Bayesian optimization tool to automatically search for optimal model hyperparameter settings.
- Parameter Reduction - Implements methods to limit trainable parameters during model adaptation to lower memory and compute requirements.
- Question Answering Systems - Implements question answering systems capable of extracting answers or solving multiple choice tasks.
- RAG Implementations - Implements the RAG architectural pattern to augment prompt context using external vector stores.
- Sequence Labeling Architectures - Includes token-level classification pipelines to assign category labels to individual words or phrases.
