Fine-tuning and training

This project is an open-source educational resource providing structured, step-by-step guides for fine-tuning large language models. It focuses on adapting pre-trained transformer-based causal models to custom datasets, enabling users to transfer specific writing styles or domain knowledge into generative AI models. The repository distinguishes itself by emphasizing parameter-efficient training techniques, specifically low-rank adaptation. By providing practical implementations for updating only a small subset of model weights, it allows for the customization of massive neural networks on consumer-grade hardware. The guides cover the entire machine learning workflow, including instruction-based dataset formatting, configuration of training parameters, and the use of gradient accumulation to manage memory constraints. The documentation provides a comprehensive technical walkthrough for the fine-tuning process, from environment setup and data preparation to model training and weight saving. It includes specific code examples for loading models in half-precision formats and configuring training arguments to optimize performance for various tasks.

Fastai is a high-level deep learning library built on PyTorch that provides a unified interface for managing the entire machine learning lifecycle. It functions as a comprehensive training toolkit, abstracting hardware management and automating complex training loops to simplify the construction and execution of neural network models. The framework is distinguished by its notebook-centric development environment and a type-dispatching data pipeline that automatically applies transformations based on input data formats. It emphasizes transfer learning through discriminative layer-wise optimization, allowing users to apply distinct learning rates and freezing strategies to specific parameter groups. A unified learner abstraction bundles data loaders, architectures, and loss functions into a single object, while a callback-based system enables the dynamic injection of custom logic into the training process. The library covers a broad capability surface, including specialized workflows for computer vision, natural language processing, and tabular data modeling. It provides extensive tools for data augmentation, model interpretation, and performance monitoring, alongside support for distributed training and mixed-precision computation to optimize resource usage. The project is designed for interactive use within Jupyter Notebooks, providing a modular ecosystem that facilitates end-to-end experimentation from initial data processing to final model deployment.

This project provides an end-to-end framework for adapting large language models to follow user instructions through supervised fine-tuning. It functions as a comprehensive training pipeline that enables the creation of specialized assistant models by minimizing the difference between predicted outputs and target responses within structured instruction datasets. The framework distinguishes itself by integrating synthetic data generation with memory-efficient training techniques. It utilizes powerful language models to iteratively expand small sets of human-written seeds into diverse, high-quality instruction-response pairs, significantly reducing the cost of data acquisition. Furthermore, it employs parameter-efficient adaptation methods, such as low-rank matrix decomposition, to update model weights with minimal computational overhead. The toolkit also includes utilities for model weight reconstruction, allowing users to apply calculated parameter offsets to base model checkpoints. This approach enables the distribution and deployment of fully functional fine-tuned models without the need to share large, complete weight files. The repository provides the necessary scripts, data generation pipelines, and evaluation procedures to support the reproduction and development of instruction-following workflows.

This project is an educational platform and research toolkit designed to teach deep learning through a combination of mathematical theory, visual diagrams, and executable code. It provides a comprehensive environment for building, training, and evaluating neural networks, grounding complex concepts in interactive computational notebooks that allow for hands-on experimentation. The framework distinguishes itself by interleaving theoretical foundations—including linear algebra, calculus, and probability—with practical implementations across multiple industry-standard libraries. It supports flexible model development through modular layer composition, deferred parameter initialization, and symbolic graph hybridization, which balances the ease of imperative coding with the performance benefits of compiled execution. The project covers a broad capability surface, including computer vision, natural language processing, recommender systems, and reinforcement learning. It provides infrastructure for data pipeline management, gradient-based optimization, and distributed training across multiple hardware accelerators. Users can leverage built-in utilities for hyperparameter tuning, model regularization, and performance monitoring to diagnose and refine their architectures. The documentation is delivered as a series of interactive notebooks that can be executed locally or on remote cloud infrastructure, providing a standardized interface for deep learning research and experimentation.

This project is a comprehensive framework for the entire lifecycle of transformer-based language models, supporting everything from foundational pretraining to specialized deployment. It provides a modular toolkit for defining neural network architectures, managing data preparation pipelines, and executing training routines across various scales. The framework is designed to handle the full model development process, including supervised fine-tuning, behavioral alignment, and the integration of agentic capabilities. What distinguishes this framework is its focus on efficient training and advanced alignment methodologies. It incorporates techniques such as low-rank parameter adaptation and mixture-of-experts routing to optimize memory usage and computational efficiency. The system also features built-in support for direct preference optimization and automated feedback training, allowing users to refine model behavior and align outputs with human intent without requiring extensive manual labeling. The platform covers a broad range of capabilities, including knowledge distillation for creating efficient student models, sequence length extrapolation for extended context processing, and robust tool-calling integration for agentic workflows. It includes utilities for benchmarking model performance, converting weights for cross-platform compatibility, and serving predictions through standardized network APIs or local command-line interfaces.

Fairseq is a PyTorch toolkit for sequence-to-sequence modeling, specializing in neural machine translation, automatic speech recognition, and large-scale language model training. It provides a framework for processing and aligning diverse data sources, including text, audio, and video, to support tasks such as speech-to-text conversion and multimodal sequence learning. The project is distinguished by its distributed training capabilities, which utilize parameter sharding, mixed-precision training, and CPU offloading to handle models that exceed single-device memory. It also includes specialized tools for data engineering, such as parallel data mining for unsupervised learning and back-translation for expanding training corpora. Its capability surface extends to comprehensive inference and generation tools, including beam search and lexical constraint enforcement, as well as model compression techniques like layer pruning and product quantization. The toolkit also provides utilities for feature extraction, model evaluation via metrics like perplexity and BLEU scores, and a registry-based system for extending models and tasks. Training and evaluation workflows are managed through a command-line interface that orchestrates hyperparameter configuration and model execution.

LlamaFactory is a unified framework for fine-tuning and adapting large language models. It provides a comprehensive platform that standardizes training workflows across diverse machine learning architectures, allowing users to execute both full-tuning and parameter-efficient methods through a single interface. The project distinguishes itself by offering a low-code visual dashboard that enables users to configure experiments and monitor performance metrics in real time without writing extensive custom scripts. It also features a configuration-driven orchestration system that decouples experiment logic from the underlying execution engine, alongside an OpenAPI-compliant server that exposes trained models as standard network endpoints for integration with external software. Beyond its core training capabilities, the platform supports real-time experiment tracking by streaming performance data to external monitoring services. This allows for the evaluation of model progress and the optimization of parameters throughout the development lifecycle. The software is designed to be installed and configured as a standalone environment for managing the end-to-end lifecycle of language model adaptation.

This project is a multimodal translation framework and large language model capable of speech-to-speech, speech-to-text, and text-to-text translation across nearly 100 languages. It provides a real-time speech translation engine and a comprehensive toolkit for converting spoken audio between languages. The system is distinguished by its ability to preserve the original speaker's tone, pace, and prosody during translation. It utilizes a specialized on-device inference toolkit that converts model checkpoints into C-based libraries, enabling low-latency execution on mobile and edge hardware without a Python runtime. The framework covers a wide range of capabilities including automatic speech recognition, expressive speech synthesis, and real-time translation streaming. It also includes audio content moderation for toxicity detection and tools for multimodal translation evaluation and distributed model fine-tuning. The project is implemented using Jupyter Notebooks.

This project is a comprehensive educational curriculum and engineering handbook focused on the lifecycle of large language models. It serves as a structured knowledge base for machine learning practitioners, covering the fundamental mathematical and architectural principles of transformer-based sequence modeling, as well as the practical implementation of supervised instruction fine-tuning and preference-based model alignment. The repository distinguishes itself by providing a deep dive into advanced model composition and optimization techniques. It details methodologies for weight-space model merging and mixture-of-experts strategies, alongside practical guidance on low-precision parameter quantization and inference optimization to manage hardware requirements. Furthermore, it explores the development of autonomous agentic systems capable of tool-use orchestration and the construction of retrieval-augmented generation pipelines to ground model outputs in external data. The content spans the entire technical stack, from foundational deep learning concepts and neural network design to the complexities of deploying, evaluating, and securing models in production environments. It includes a curated collection of technical articles, blog posts, and interactive notebooks that track state-of-the-art research trends and experimental methodologies in generative artificial intelligence.

Co-tracker is a PyTorch point tracking framework and dense point tracking model designed to map the motion of individual pixels throughout a video. It functions as a video pixel tracker that predicts point trajectories and visibility masks across sequences of video frames. The project includes a computer vision training pipeline that utilizes teacher-student knowledge distillation. This allows for the generation of pseudo-labels from unannotated real video data to fine-tune pre-trained models and reduce the gap between synthetic and real data environments. The framework provides capabilities for video motion analysis and visual tracking evaluation, including tools for rendering trajectories and visibility masks to inspect tracking accuracy. It supports both offline context and online streaming processing for video sequence analysis.

Unsloth is a high-performance training and inference platform designed to optimize the lifecycle of large language and multimodal models. It provides a comprehensive engine for fine-tuning, executing, and managing models locally, with a focus on reducing memory consumption and increasing compute speed on consumer-grade hardware. The platform distinguishes itself through hand-optimized kernels and automated computational graph techniques that maximize hardware throughput. It supports advanced training methodologies, including reinforcement learning for reasoning and efficient adapter-based fine-tuning, while offering a unified web-based interface for no-code model training, data preparation, and real-time performance monitoring. Beyond its core training capabilities, the project includes a local inference runtime that supports API-based deployment, tool-calling, and automated output verification. It manages the entire model development process, from dataset generation and hyperparameter configuration to model exporting and performance benchmarking across diverse hardware configurations. The software provides setup utilities for local development environments and includes diagnostic tools to assist with installation and hardware compatibility.

This project is a modular PyTorch framework for training and evaluating object detection and instance segmentation models. It serves as a computer vision research tool and a deep learning inference engine designed to identify object locations, classes, and pixel-level masks within images. The framework implements a two-stage inference pipeline that utilizes region proposal networks and a symmetric mask-head architecture. It provides specialized capabilities for instance segmentation, object bounding box detection, and human pose estimation via anatomical keypoint detection. The system includes comprehensive data engineering utilities for parsing COCO datasets, managing custom dataset integration, and performing annotation filtering. It covers the full machine learning workflow, including custom model training with GPU acceleration, weight fine-tuning, batch inference execution, and the calculation of accuracy metrics.

This project is a comprehensive library of state-of-the-art neural network architectures designed for image classification and feature extraction. It provides a complete deep learning training framework that supports distributed execution, allowing users to build, train, and fine-tune vision models using optimized schedulers and pre-configured training recipes. The library distinguishes itself through a modular backbone architecture that treats neural networks as decoupled feature extractors, enabling the retrieval of multi-scale outputs for downstream tasks like object detection and segmentation. A centralized registry-based model factory allows for the dynamic instantiation of architectures via string identifiers, while externalized hyperparameter files ensure that training workflows remain reproducible. Users can also exercise granular control over the training process through layer-wise optimization configurations and a flexible hook system for intercepting intermediate tensor states. The platform includes extensive utilities for managing the entire lifecycle of a vision model, from data loading and augmentation to inference and deployment. It features a dynamic transformation pipeline that automatically resolves preprocessing requirements based on the chosen model architecture, ensuring that input data is correctly aligned for both training and evaluation. Integration with remote model hubs further facilitates the sharing and retrieval of pre-trained weights and configurations.

This repository is a comprehensive set of tutorials and examples for building software powered by large language models. It serves as an application development guide and a prompt engineering framework, providing instructional content for integrating model logic with user interfaces and external data sources. The project provides technical walkthroughs for specialized workflows, including the implementation of retrieval augmented generation using vector databases and semantic search. It includes guidance on adapting pre-trained model weights through fine-tuning with private datasets and the orchestration of autonomous agents that connect language models to external tools and APIs. The material covers a broad range of AI development capabilities, including prompt optimization for summarization and inference, the deployment of generative AI interfaces, and the systematic evaluation of model outputs for quality and consistency.

Qwen3 is a transformer-based large language model designed as a generative AI foundation for understanding, reasoning, and generating human language. It functions as a comprehensive ecosystem for model training, fine-tuning, and production-ready inference, providing the underlying architecture and weights necessary to build diverse artificial intelligence applications. The project distinguishes itself through extensive support for model quantization and distributed inference, enabling efficient execution across a wide range of hardware from consumer-grade devices to scalable cloud infrastructure. It includes a specialized toolkit for weight compression and memory optimization, such as key-value cache management, which reduces computational requirements while maintaining performance. Furthermore, the model integrates with agentic frameworks, allowing for the development of autonomous systems capable of executing complex workflows and interacting with external tools. The ecosystem covers a broad surface of deployment and training methodologies, including standardized interfaces for modular plugin integration and function calling. It provides extensive documentation for various training, fine-tuning, and serving environments to facilitate integration into existing software stacks.

Audiocraft is a deep learning audio library and machine learning framework designed for training, fine-tuning, and evaluating generative models for music and sound effects. It functions as a text-to-music generative model and a neural audio codec, providing the tools necessary to compress audio signals into discrete representations and synthesize high-fidelity waveforms from textual descriptions. The framework is distinguished by its ability to combine multiple conditioning signals, allowing for the generation of audio based on text prompts, melodic excerpts, or style-based audio clips. It also includes a specialized audio watermarking tool for embedding and detecting invisible markers within signals to protect ownership and track content origins. The project covers a broad range of capabilities, including neural audio compression, audio data augmentation, and the execution of complex training pipelines for diffusion and masked audio models. It provides utilities for model lifecycle management, such as checkpoint exporting and experiment tracking, alongside evaluation metrics for measuring signal fidelity and perceptual quality.

PyTorch Lightning is a deep learning research framework that provides a structured environment for organizing machine learning code. It functions as a unified trainer orchestrator, centralizing the execution flow by managing the interaction between hardware resources, data loaders, and model components. By decoupling model architecture from training logic, the framework enables researchers to maintain clean, modular codebases that remain portable across different environments. The framework distinguishes itself through a hardware-agnostic abstraction layer that scales deep learning workloads across multiple accelerators without requiring manual management of parallelization or synchronization logic. It utilizes a hook-based execution lifecycle and a plugin system to inject custom behaviors, such as logging, checkpointing, and early stopping, directly into the training loop. This modular approach allows developers to extend training functionality without modifying the underlying core application code. Beyond its core orchestration capabilities, the project enforces a standardized structure for training pipelines to simplify collaboration and improve experiment reproducibility. It includes state-based serialization to capture the full training state, ensuring that sessions can be consistently resumed after interruptions. The framework is distributed as a Python package and provides a consistent class-based interface for managing complex machine learning workflows.

IF is a text-to-image diffusion system that translates natural language descriptions into visual imagery. The project provides a generative pipeline for creating images, an inpainting tool for modifying specific image sections, and a super-resolution upscaler to increase pixel density and clarity. The system includes a concept fine-tuning framework that allows for the teaching of new visual concepts by updating a small set of parameters. It also supports image style transfer to apply the aesthetic characteristics of a reference image to a new output.

Open-r1 is a framework designed for the large-scale training, distillation, and optimization of language models focused on complex reasoning and programming tasks. It provides a comprehensive suite of tools for managing distributed training jobs across multi-node clusters, enabling the development of high-performance models through reinforcement learning and supervised fine-tuning. The project distinguishes itself by integrating secure, containerized code execution environments directly into the training and evaluation lifecycle. By allowing models to run and verify code snippets against test cases, the framework improves accuracy in mathematical and logical problem-solving. It further supports advanced reasoning capabilities through group relative policy optimization and automated synthetic data pipelines, which curate and filter high-quality reasoning traces for model updates. The system utilizes modular, configuration-driven recipes to streamline complex workflows, including data decontamination, dataset composition, and multi-node orchestration. It includes standardized benchmarking tools to measure performance across reasoning and coding domains, ensuring that training processes remain reproducible and data-centric. The framework is built to handle the full lifecycle of model improvement, from initial synthetic data generation to final performance evaluation on high-performance computing clusters.

FinGPT is a suite of specialized financial tools and a framework for adapting large language models to the financial domain. It provides a set of pipelines for financial entity extraction, sentiment analysis, and retrieval-augmented generation to improve the accuracy of financial information systems. The project distinguishes itself through efficient training workflows, utilizing low-rank adaptation and quantized low-rank adaptation to fine-tune models on consumer-grade hardware. It employs market-labeled datasets and reinforcement learning that uses actual stock price movements as reward signals to refine model performance. The framework covers broad capability areas including algorithmic trading signal generation, automated investment research, and stock price movement prediction. It also provides tools for collecting global financial data and generating source code for quantitative trading factors. The project is primarily implemented and demonstrated through Jupyter Notebooks.