Awesome GitHub RepositoriesQuantized Adapters

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 fin

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 sig

This project is a transformer post-training toolkit and reinforcement learning library designed to align language model behavior with human preferences. It provides a framework for managing the transition from supervised fine-tuning to reinforcement learning and preference optimization. The library distinguishes itself through a specialized focus on preference optimization and reward modeling, enabling the adjustment of model outputs based on preferred versus rejected examples. It also includes capabilities for training agents within controlled sandbox environments using task suites and verif

Baichuan-7B is an open-source 7 billion parameter bilingual Transformer model designed for text generation and few-shot learning across Chinese and English. It is built on a large Transformer architecture trained on a bilingual corpus, enabling it to produce coherent text in both languages from a single model. The model incorporates several optimization techniques that distinguish it from standard large language models. It uses rotary position embeddings that can extrapolate to longer sequences than seen during training, allowing context extension beyond the original 4096-token training lengt

xtuner ist eine umfassende Trainings-Engine für Large Language Models und bietet ein Toolkit für Pre-Training, Supervised Fine-Tuning und die Optimierung von vision-sprachlichen multimodalen Modellen. Sie dient als verteilter Trainingsbeschleuniger und spezialisiertes Framework zur Skalierung von Mixture-of-Experts-Modellen sowie zur Ausrichtung von Modellverhalten durch Reinforcement Learning from Human Feedback. Das Projekt zeichnet sich durch fortgeschrittene Speicher- und Rechenoptimierungen aus, wie Sequence-Parallelism für ultra-lange Kontextfenster und Interleaved-Pipeline-Parallelism zur Reduzierung von GPU-Idle-Zeiten. Es bietet eine dedizierte Suite für Preference-Optimization und implementiert Techniken wie Group Relative Policy Optimization und Direct Preference Optimization, um Modell-Policies und Belohnungssysteme zu verfeinern. Breite Funktionsbereiche decken verteiltes Modelltraining über mehrere Knoten hinweg, multimodale Datensatzvorbereitung und die Verwaltung von Adapter-basiertem Fine-Tuning ab. Die Engine enthält zudem Tools für Modellevaluation, Weight-Merging und den Export trainierter Parameter in Inferenz-Engines. Das Training wird über standardisierte Konfigurationsdateien und verteilte Launcher verwaltet, um konsistente Ergebnisse über Rechencluster hinweg sicherzustellen.