# zyds/transformers-code **Attribution required: if you use, quote, or summarise this content, you must credit and link back to [awesome-repositories.com](https://awesome-repositories.com/repository/zyds-transformers-code).** 3,782 stars · 497 forks · Jupyter Notebook ## Links - GitHub: https://github.com/zyds/transformers-code - awesome-repositories: https://awesome-repositories.com/repository/zyds-transformers-code.md ## Topics `huggingface` `peft` `transformers` ## Description 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. ## Tags ### Artificial Intelligence & ML - [Large Language Model Training Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-training-and-tuning/training-frameworks/large-language-model-training-frameworks.md) — Provides a comprehensive framework for training and optimizing large language models across multi-GPU environments. - [Language Model Training](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/model-fine-tuning-adaptation/language-model-training.md) — Provides tools and techniques for training large language models to generate coherent and contextually relevant text. ([source](https://github.com/zyds/transformers-code/tree/master/02-NLP%20Tasks)) - [Knowledge Base Retrieval](https://awesome-repositories.com/f/artificial-intelligence-ml/agentic-rag-development/knowledge-base-retrieval.md) — Supports generating accurate responses by retrieving relevant information from an external knowledge base. ([source](https://github.com/zyds/transformers-code/tree/master/02-NLP%20Tasks)) - [Retrieval-Augmented Generation](https://awesome-repositories.com/f/artificial-intelligence-ml/conversational-interfaces/retrieval-augmented-generation.md) — Builds conversational agents using retrieval-augmented generation to integrate external knowledge bases. - [Distributed Training Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/distributed-training-frameworks.md) — Ships a distributed training framework to scale LLM workloads using data parallelism and quantization. - [Data-Parallel Training](https://awesome-repositories.com/f/artificial-intelligence-ml/distributed-training-frameworks/data-parallel-training.md) — Implements distributed data-parallel training to scale workloads across multiple graphics processors. - [Distributed Training](https://awesome-repositories.com/f/artificial-intelligence-ml/distributed-training-frameworks/distributed-training.md) — Provides a framework for distributing model training across multiple hardware accelerators. ([source](https://github.com/zyds/transformers-code#readme)) - [Conversational Response Generation](https://awesome-repositories.com/f/artificial-intelligence-ml/language-model-response-generators/response-generation-configurations/conversational-response-generation.md) — Enables the generation of fluid, human-like conversational responses using prompt and interaction history. ([source](https://github.com/zyds/transformers-code/tree/master/02-NLP%20Tasks)) - [Mixed Precision Training](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/machine-learning-training/distributed-and-accelerated-compute/training-acceleration-tools/mixed-precision-training.md) — Utilizes mixed-precision training and low-precision formats to reduce memory and increase throughput. ([source](https://github.com/zyds/transformers-code#readme)) - [Mixed-Precision Quantization](https://awesome-repositories.com/f/artificial-intelligence-ml/model-optimization/compression-techniques/model-pruning/model-compression-suites/half-precision-compression/mixed-precision-quantization.md) — Employs mixed-precision quantization to reduce memory usage and accelerate training speed. - [Multi-GPU Training Utilities](https://awesome-repositories.com/f/artificial-intelligence-ml/multi-gpu-training-utilities.md) — Enables scaling of model training across multiple GPUs using parallel data strategies. ([source](https://github.com/zyds/transformers-code/blob/master/README.md)) - [Token Classification](https://awesome-repositories.com/f/artificial-intelligence-ml/natural-language-classification/token-classification.md) — Identifies and categorizes individual words or phrases within a text into predefined labels. ([source](https://github.com/zyds/transformers-code/tree/master/02-NLP%20Tasks)) - [Natural Language Processing](https://awesome-repositories.com/f/artificial-intelligence-ml/natural-language-processing.md) — Executes a wide range of NLP tasks including summarization and sentence similarity measurement. - [Text Summarization](https://awesome-repositories.com/f/artificial-intelligence-ml/natural-language-processing/nlp-applications/text-summarization.md) — Condenses long documents into shorter versions while preserving core meaning and essential information. ([source](https://github.com/zyds/transformers-code/tree/master/02-NLP%20Tasks)) - [Parameter Efficient Fine-Tuning](https://awesome-repositories.com/f/artificial-intelligence-ml/parameter-efficient-fine-tuning.md) — Supports parameter-efficient fine-tuning to adapt large models with minimal trainable parameters. - [Sequence-to-Sequence Transformer Architectures](https://awesome-repositories.com/f/artificial-intelligence-ml/sequence-to-sequence-transformer-architectures.md) — Utilizes transformer-based sequence modeling and self-attention mechanisms to process text sequences. - [Transformer-Based NLP Libraries](https://awesome-repositories.com/f/artificial-intelligence-ml/transformer-based-nlp-libraries.md) — Provides a library of pre-configured transformer-based pipelines for various NLP task implementations. - [Transformer Training Toolkits](https://awesome-repositories.com/f/artificial-intelligence-ml/transformer-training-toolkits.md) — Provides a specialized toolkit for the full lifecycle of transformer model training and deployment. - [AI Chatbots](https://awesome-repositories.com/f/artificial-intelligence-ml/ai-chatbots.md) — Develops generative AI chatbots that produce human-like responses based on interaction histories. - [Hyperparameter Optimization Tools](https://awesome-repositories.com/f/artificial-intelligence-ml/hyperparameter-optimization-tools.md) — Includes a Bayesian optimization tool for automatically tuning language model training parameters. - [Hyperparameter Optimization](https://awesome-repositories.com/f/artificial-intelligence-ml/model-optimization/training-efficiency/hyperparameter-optimization.md) — Implements automated hyperparameter optimization using Bayesian frameworks to maximize model performance. - [Bayesian Optimization](https://awesome-repositories.com/f/artificial-intelligence-ml/neural-architecture-search/bayesian-optimization.md) — Provides a Bayesian optimization tool to automatically search for optimal model hyperparameter settings. - [Parameter Reduction](https://awesome-repositories.com/f/artificial-intelligence-ml/parameter-efficient-fine-tuning/parameter-adaptation-techniques/parameter-reduction.md) — Implements methods to limit trainable parameters during model adaptation to lower memory and compute requirements. ([source](https://github.com/zyds/transformers-code#readme)) - [Question Answering Systems](https://awesome-repositories.com/f/artificial-intelligence-ml/question-answering-systems.md) — Implements question answering systems capable of extracting answers or solving multiple choice tasks. - [RAG Implementations](https://awesome-repositories.com/f/artificial-intelligence-ml/rag-implementations.md) — Implements the RAG architectural pattern to augment prompt context using external vector stores. - [Sequence Labeling Architectures](https://awesome-repositories.com/f/artificial-intelligence-ml/sequence-labeling-architectures.md) — Includes token-level classification pipelines to assign category labels to individual words or phrases. ### Part of an Awesome List - [Question Answering](https://awesome-repositories.com/f/awesome-lists/ai/question-answering.md) — Enables extracting or generating specific answers from a given text based on user queries. ([source](https://github.com/zyds/transformers-code/tree/master/02-NLP%20Tasks))