# Efficient LLM Fine-Tuning with LoRA > Search results for `fine-tune LLMs efficiently with LoRA and QLoRA` on awesome-repositories.com. 109 total matches; showing the first 50. Explore on the web: https://awesome-repositories.com/q/fine-tune-llms-efficiently-with-lora-and-qlora **Attribution required: if you use, quote, or summarise this content, you must credit and link back to [this search on awesome-repositories.com](https://awesome-repositories.com/q/fine-tune-llms-efficiently-with-lora-and-qlora).** ## Results - [hiyouga/llama-efficient-tuning](https://awesome-repositories.com/repository/hiyouga-llama-efficient-tuning.md) (72,239 ⭐) — This project is a fine-tuning framework and training pipeline designed to optimize and adapt large language and vision models. It provides a specialized toolkit for parameter-efficient tuning and supervised learning, serving as both a trainer for multimodal models and a deployment tool for serving fine-tuned models via high-performance inference engines. The framework focuses on reducing memory and compute requirements by updating a small subset of model parameters. It supports a wide range of adaptation strategies, including vision-language model training to align text, image, video, and audio data, as well as preference alignment to match model behavior with human expectations. The system covers a broad set of capabilities including supervised fine-tuning, instruction tuning, and core pre-training. It incorporates memory optimization through quantization and weight-merging pipelines, alongside data management for importing and preparing custom datasets. For operational management, it includes a web-based interface for task execution and integration with external dashboards for experiment metric tracking. The project provides utilities for exporting model checkpoints and deploying tuned models as web services using standardized, OpenAI-compatible API interfaces. - [microsoft/lora](https://awesome-repositories.com/repository/microsoft-lora.md) (13,264 ⭐) — LoRA is a framework for parameter-efficient fine-tuning of large-scale neural networks. It functions by injecting trainable low-rank decomposition matrices into frozen model layers, allowing for task-specific adaptation while preserving the integrity of the original base model weights. The project distinguishes itself by enabling the direct merging of these trained low-rank matrices into primary model weights. This process eliminates additional computational overhead during inference, ensuring that adapted models maintain the same performance characteristics as the original architecture. Furthermore, the framework supports modular adaptation, allowing users to swap between different task-specific configurations by loading and unloading lightweight matrices without modifying the underlying model. The toolkit provides comprehensive support for optimizing the entire model lifecycle, including storage-efficient checkpointing and targeted updates to bias vectors. By training only a small fraction of the total parameters, the library reduces the disk space required for model storage and facilitates the deployment of adapted states across diverse hardware systems. - [axolotl-ai-cloud/axolotl](https://awesome-repositories.com/repository/axolotl-ai-cloud-axolotl.md) (12,059 ⭐) — Axolotl is a configuration-driven framework designed for the fine-tuning, evaluation, and quantization of large language models. It functions as a comprehensive orchestrator for distributed training, enabling users to manage complex workflows across multi-node and multi-GPU environments. By utilizing structured configuration files, the platform streamlines the setup of training parameters, dataset paths, and hardware distribution strategies. The project distinguishes itself through its support for diverse training methodologies, including full-parameter tuning, parameter-efficient adaptation, and reinforcement learning alignment. It provides specialized capabilities for multimodal model training, allowing for the integration of text, image, and media inputs. Furthermore, the framework includes advanced optimization tools such as quantization-aware training, which simulates precision loss to maintain model accuracy, and dynamic reward signal integration for aligning model behavior with human preferences. The framework covers a broad capability surface, including data management, performance optimization, and model lifecycle management. It handles data ingestion, preprocessing, and streaming, while offering advanced techniques like sequence packing and replay buffers to improve training efficiency. Performance is managed through distributed parallelism strategies, memory-efficient training pipelines, and custom kernel implementations. The project provides pre-configured container images to ensure consistent deployment across local and cloud-based compute environments. Users can manage the entire model lifecycle, from initial configuration and training to adapter merging and final inference execution. - [huggingface/peft](https://awesome-repositories.com/repository/huggingface-peft.md) (21,274 ⭐) — This library provides a framework for parameter-efficient fine-tuning, enabling the adaptation of large pretrained models by training only a small subset of parameters. It functions as a distributed model training system and optimization toolkit, designed to reduce the computational and memory requirements typically associated with full model fine-tuning. The project distinguishes itself through a suite of methods for modular adapter composition, including low-rank matrix decomposition and activation-based scaling. It supports the integration of multiple task-specific adapter modules, allowing users to merge, route, and combine these components into base model architectures. To ensure efficient inference, the library provides capabilities to integrate trained adapter weights directly into the original model. The framework includes extensive support for memory-optimized training, utilizing techniques such as parameter offloading to system memory, low-bit quantization, and distributed parameter sharding across multiple hardware devices. These features allow for the training of massive models that exceed the memory capacity of individual graphics processing units. The library is distributed as a Python package and includes command-line tools for managing training tasks and authentication. - [hiyouga/chatglm-efficient-tuning](https://awesome-repositories.com/repository/hiyouga-chatglm-efficient-tuning.md) (3,720 ⭐) — Fine-tuning ChatGLM-6B with PEFT | 基于 PEFT 的高效 ChatGLM 微调 - [kvcache-ai/ktransformers](https://awesome-repositories.com/repository/kvcache-ai-ktransformers.md) (17,288 ⭐) — Ktransformers is a comprehensive framework designed for the operation, fine-tuning, and serving of large language models. It functions as a heterogeneous inference engine and quantized execution runtime, enabling the deployment of massive models by distributing computational workloads across both CPU and GPU resources. This architecture allows users to bypass local memory constraints, making it possible to run and train models that exceed the capacity of a single device. The project distinguishes itself through specialized support for sparse architectures, particularly mixture-of-experts models. It employs pipelined expert offloading and layer-wise sharding to balance memory usage and processing speed across heterogeneous hardware. By utilizing hardware-specific kernel optimizations, such as specialized instruction sets for server processors, the framework maximizes throughput for both inference and fine-tuning tasks. Beyond its core execution capabilities, the project provides a production-ready serving environment that exposes models via an OpenAI-compatible HTTP interface. It includes a suite of command-line tools for managing model deployments, configuring system environments, and performing performance benchmarking. The framework also supports the integration of custom inference kernels and operator injection, allowing for architectural modifications and fine-tuned control over model placement strategies. - [km1994/llms_interview_notes](https://awesome-repositories.com/repository/km1994-llms-interview-notes.md) (2,567 ⭐) — This repository is a collection of notes and resources focused on large language models (LLMs), specifically curated for interview preparation. It serves as a study guide covering the key concepts, architectures, and practical knowledge needed to discuss LLMs in a technical interview setting. The material spans the fundamental topics relevant to understanding and working with LLMs, including their underlying mechanisms, training processes, and evaluation methods. The notes are organized to help readers build a structured understanding of the field, from foundational principles to more advanced considerations. - [lordog/dive-into-llms](https://awesome-repositories.com/repository/lordog-dive-into-llms.md) (40,974 ⭐) — Dive into LLMs is a framework designed for fine-tuning large language models and constructing modular machine learning pipelines. It provides a structured environment for adjusting pre-trained models on custom datasets while optimizing computational efficiency and training time. The project distinguishes itself by offering an interactive web interface that allows for the deployment and publication of trained models directly to a browser. This enables users to test and interact with model results through a standardized web-based environment. The platform supports the creation of flexible workflows by separating data processing, model architecture, and evaluation into independent stages. These capabilities are delivered through a collection of Jupyter Notebooks that facilitate the development and maintenance of specialized artificial intelligence solutions. - [ludwig-ai/ludwig](https://awesome-repositories.com/repository/ludwig-ai-ludwig.md) (11,717 ⭐) — Ludwig is a multimodal machine learning platform and low-code framework designed for building, training, and deploying neural networks. It enables the construction of models that process text, images, audio, and tabular data through a unified interface using declarative configuration files rather than custom code. The system features a specialized low-code framework for large language models, supporting supervised fine-tuning, preference alignment, and a constrained decoding tool to force structured data output via logit extraction. It also includes an automated model architecture search to identify optimal encoder and combiner combinations for specific datasets. The platform provides a distributed model training engine to scale workloads across compute clusters and containerized environments. Its capabilities extend to computer vision tasks like semantic segmentation, time-series forecasting, and a deployment pipeline that exports models as high-performance REST APIs for real-time inference. The project includes a command-line interface for executing training and evaluation tasks within provisioned container images. - [holms-ur/fine-tuning](https://awesome-repositories.com/repository/holms-ur-fine-tuning.md) (72 ⭐) — Close-Domain fine-tuning for table detection - [mudler/localai](https://awesome-repositories.com/repository/mudler-localai.md) (46,889 ⭐) — LocalAI is a self-hosted inference server that enables the execution of machine learning models directly on local hardware. By providing a unified interface for text, image, and audio processing, it allows users to maintain full control over data privacy and infrastructure costs while eliminating dependencies on external network services. The platform functions as an API gateway that mimics standard cloud-based artificial intelligence interfaces, allowing existing applications to integrate local models as drop-in replacements. It utilizes a container-based architecture to package runtimes and dependencies, ensuring consistent deployment across diverse hardware configurations. To optimize system performance, the server employs an on-demand orchestration layer that dynamically loads and unloads models based on active requests, minimizing memory usage during periods of inactivity. The system supports a wide range of model architectures through a flexible backend abstraction that allows for driver switching at runtime. Users can manage their models and interact with the service through a web interface or via standard web requests, which the proxy translates into model-specific execution commands. The software is distributed as a containerized application to facilitate deployment across various server and cloud environments. - [modelscope/diffsynth-studio](https://awesome-repositories.com/repository/modelscope-diffsynth-studio.md) (12,585 ⭐) — DiffSynth-Studio is a comprehensive platform for the lifecycle management of generative diffusion models, providing a unified environment for inference, fine-tuning, and training. It utilizes a modular pipeline architecture and a standardized abstraction layer to support consistent workflows across diverse model configurations for image and video generation. The platform distinguishes itself through a memory-optimized inference engine that dynamically manages resources to facilitate high-resolution generation on constrained hardware. It also integrates specialized training capabilities, including low-rank adaptation techniques, which allow for the efficient adjustment of large models to specific datasets or visual styles. Beyond core generation and training, the system includes automated evaluation frameworks that apply objective metrics to assess the aesthetic quality and prompt alignment of generated media. These tools are accessible through a command-line interface designed to automate the execution and monitoring of complex generative workflows. - [tsinghuac3i/intuitive-fine-tuning](https://awesome-repositories.com/repository/tsinghuac3i-intuitive-fine-tuning.md) (0 ⭐) — This repository contains the code for the paper "Intuitive Fine-Tuning: Towards Simplifying Alignment into a Single Process". - [mlabonne/llm-course](https://awesome-repositories.com/repository/mlabonne-llm-course.md) (80,178 ⭐) — 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. - [alirezadir/machine-learning-interviews](https://awesome-repositories.com/repository/alirezadir-machine-learning-interviews.md) (8,455 ⭐) — This project is a comprehensive machine learning interview guide and technical study resource designed for individuals preparing for machine learning and AI engineering roles. It provides a collection of materials and practice problems covering core algorithms, theoretical fundamentals, and the implementation of neural network architectures. The resource serves as a technical reference for generative AI development, focusing on the design and optimization of large language models and diffusion systems. It includes frameworks for system design, covering the architecture of production machine learning pipelines, retrieval pipelines, agentic workflows, and the reduction of latency and memory footprints through inference optimization. Beyond model architecture, the project covers MLOps deployment workflows, including A/B testing and canary releases, as well as model evaluation and validation strategies. It also provides coaching for behavioral interviews, utilizing structured communication frameworks to handle professional and situational questions. The project is implemented as a collection of Jupyter Notebooks. - [zai-org/chatglm-6b](https://awesome-repositories.com/repository/zai-org-chatglm-6b.md) (41,039 ⭐) — ChatGLM-6B is a generative AI inference engine designed for local execution of transformer-based language models. It provides a comprehensive runtime environment that allows users to load and run pre-trained neural network weights directly on their own hardware, ensuring data privacy and independence from external cloud services. The project distinguishes itself through a hardware-agnostic execution backend that supports deployment across diverse environments, including standard processors, Apple Silicon, and multi-GPU configurations. It incorporates advanced optimization techniques such as weight quantization and parameter-efficient fine-tuning via low-rank adaptation, which significantly reduce memory requirements and computational overhead. These features enable the deployment of large models on consumer-grade hardware while maintaining high throughput and performance. Beyond core inference, the toolkit includes a suite of utilities for programmatic integration, allowing developers to embed model capabilities into custom software workflows via standard interfaces. It also provides multiple interactive interfaces, including web-based graphical environments for text and vision tasks and a command-line interface for rapid prototyping and evaluation. The software is distributed as a Python-based package, requiring standard environment configuration to manage dependencies and hardware resource allocation. - [alibaba/roll](https://awesome-repositories.com/repository/alibaba-roll.md) (2,844 ⭐) — ROLL is a distributed reinforcement learning framework and model alignment toolkit designed for large language models. It serves as a scalable training pipeline and GPU cluster manager, providing the infrastructure to align model behavior using reinforcement learning algorithms and preference optimization techniques. The project distinguishes itself through an agentic rollout orchestrator that generates and collects multi-turn interaction trajectories between AI agents and simulated environments. It supports specialized alignment methods including Direct Preference Optimization, reinforcement learning from verifiable rewards, and group-relative reward optimization. The framework covers a broad range of capabilities for large-scale distributed training, including tensor, pipeline, and expert parallelism to support ultra-large-scale models. It manages hardware resources through GPU multiplexing and disaggregated deployment, while providing tools for automated reward evaluation using code sandboxes and mathematical verification. Pre-configured environment deployments are provided for different GPU architectures and library versions to accelerate setup. - [huggingface/transformers](https://awesome-repositories.com/repository/huggingface-transformers.md) (161,630 ⭐) — Transformers is a comprehensive library for machine learning that provides a unified interface for training, fine-tuning, and deploying transformer-based models. It supports a wide range of tasks, including text classification, language modeling, question answering, and sequence-to-sequence translation, while offering specialized architectures for both text and vision processing. The framework includes tools for managing the entire model lifecycle, from data preprocessing and tokenization to distributed training and inference. The library features extensive support for model optimization and performance, including techniques like quantization, speculative decoding, and paged memory management for key-value caches. It provides native integration for distributed training across multi-node clusters, as well as flexible APIs for serving models via compatible inference servers. Developers can also utilize built-in utilities for model patching, custom kernel execution, and automated documentation generation to streamline development workflows. - [llm-tuning-safety/llms-finetuning-safety](https://awesome-repositories.com/repository/llm-tuning-safety-llms-finetuning-safety.md) (0 ⭐) — Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To! - [aishwaryanr/awesome-generative-ai-guide](https://awesome-repositories.com/repository/aishwaryanr-awesome-generative-ai-guide.md) (24,755 ⭐) — This project is a community-driven knowledge repository and technical learning resource focused on the field of generative artificial intelligence. It serves as a centralized hub for developers and practitioners to access curated research, tutorials, and foundational concepts necessary for building and deploying modern artificial intelligence applications. The platform distinguishes itself through a collaborative, distributed contribution model that aggregates diverse learning materials into a structured, searchable knowledge base. It covers a wide range of specialized topics, including retrieval-augmented generation, large language model training, fine-tuning techniques, and agentic workflows. Beyond technical skill development, the repository functions as a professional development hub, offering interview preparation resources and guidance for those pursuing careers in the artificial intelligence industry. The content is organized through a hierarchical taxonomy, allowing users to navigate complex subjects such as system evaluation, multimodal models, and security tools. The repository provides access to comprehensive code notebooks and structured tutorials, all maintained as static documentation within a version control system to ensure accessibility and ease of discovery. - [xamey/deploy-llms-with-ansible](https://awesome-repositories.com/repository/xamey-deploy-llms-with-ansible.md) (3 ⭐) — Easily deploy LLMs with Ansible. Uses Docker with llama.cpp or ollama. Secured with whitelisted IPs. - [kohya-ss/sd-scripts](https://awesome-repositories.com/repository/kohya-ss-sd-scripts.md) (7,133 ⭐) — sd-scripts is a suite of utilities designed for fine-tuning generative models, preprocessing datasets, and converting model weights. It provides a collection of scripts for executing Stable Diffusion training through methods such as DreamBooth, textual inversion, and full fine-tuning, alongside a framework for creating and managing Low-Rank Adaptation weights. The project features specialized capabilities for model weight conversion between different architectures and precision formats. It includes tools for merging adaptation weights into base models, extracting weights from trained models, and integrating multiple adaptation modules to blend styles or concepts. The toolkit covers a broad range of generative AI operations, including image dataset preparation with automated tagging and aspect ratio bucketing, and various inference methods such as text-to-image, image-to-image, and inpainting. It also incorporates memory and performance optimizations, including VRAM management, latent caching, and just-in-time training acceleration. The software provides integration for synchronizing training states and model checkpoints with the Hugging Face Hub. - [artidoro/qlora](https://awesome-repositories.com/repository/artidoro-qlora.md) (10,929 ⭐) — This project is a quantized fine-tuning framework for large language models. It implements a low-rank adaptation library and a four-bit quantizer to reduce the GPU memory requirements needed to train large models. The framework utilizes four-bit quantization and low-rank adapters to enable model training on consumer-grade hardware. It further reduces the memory footprint through double quantization and a paged optimizer that offloads states to system RAM. The system supports distributed training across multiple GPUs to handle larger parameter scales and includes utilities for custom dataset loading. It also provides automated generation scoring to evaluate model performance against benchmarks. - [optimalscale/lmflow](https://awesome-repositories.com/repository/optimalscale-lmflow.md) (8,488 ⭐) — LMFlow is a comprehensive suite for large language model fine-tuning, context extension, multimodal processing, and inference execution. It provides a toolkit for updating model parameters through full tuning or memory-efficient adapter algorithms, alongside an inference engine for executing tuned models via command-line or web-based interfaces. The framework includes a dedicated alignment suite for supervised tuning and reward model training to refine model behavior. It features a context window extender to increase maximum input lengths and a multimodal framework for building chatbots that process and generate responses from combined image and text inputs. The project covers broad capability areas including domain-specific and instruction-following fine-tuning, vocabulary expansion, and model performance benchmarking. It also incorporates memory optimization techniques, low-bit weight quantization for inference acceleration, and utilities for conversation formatting and training data ingestion. - [aiot-mlsys-lab/efficient-llms-survey](https://awesome-repositories.com/repository/aiot-mlsys-lab-efficient-llms-survey.md) (1,260 ⭐) — [TMLR 2024] Efficient Large Language Models: A Survey - [jxhe/unify-parameter-efficient-tuning](https://awesome-repositories.com/repository/jxhe-unify-parameter-efficient-tuning.md) (0 ⭐) — Copyright 2020 The HuggingFace Team. All rights reserved. - [yangjianxin1/firefly](https://awesome-repositories.com/repository/yangjianxin1-firefly.md) (6,642 ⭐) — Firefly is a training framework and inference engine for large language models. It functions as a toolkit for pre-training and fine-tuning various open-weight architectures, providing a system for model alignment and parameter-efficient fine-tuning. The project includes utilities for merging adapter weights back into base models to create standalone files. It also provides a model alignment toolkit to format training data according to specific prompt templates, ensuring conversational consistency across different models. The framework supports distributed model training and preference-based optimization. Inference capabilities include multi-turn dialogue execution with low-precision memory optimizations to reduce hardware requirements. - [hiyouga/llama-factory](https://awesome-repositories.com/repository/hiyouga-llama-factory.md) (72,241 ⭐) — LLaMA-Factory is a comprehensive suite for dataset preparation, model fine-tuning, memory optimization, and standardized API deployment. It provides a unified platform for the supervised and reward-based fine-tuning of large language models and vision-language models. The framework includes a specialized toolkit for training vision-language models and a model serving interface that deploys trained models through high-performance APIs. It utilizes precision tuning and quantization techniques to reduce the hardware requirements and memory footprint of large models. The system covers data pipeline management for local and cloud datasets, distributed training backends, and parameter-efficient fine-tuning. It also incorporates experiment monitoring to track and visualize training progress and performance metrics through external dashboards. - [thudm/p-tuning-v2](https://awesome-repositories.com/repository/thudm-p-tuning-v2.md) (2,078 ⭐) — An optimized deep prompt tuning strategy comparable to fine-tuning across scales and tasks - [samsungsailmontreal/tinyrecursivemodels](https://awesome-repositories.com/repository/samsungsailmontreal-tinyrecursivemodels.md) (6,540 ⭐) — TinyRecursiveModels is a recursive training framework for small neural networks designed to solve complex logical tasks. It functions as a parameter-efficient model trainer and a reasoning dataset generator, enabling the optimization of models that refine their answers through iterative reasoning steps. The framework differentiates itself by utilizing latent-state recursive refinement, where the model maintains and updates an internal hidden representation to improve prediction accuracy over multiple sequential steps. It also includes tools for generating structured training and evaluation datasets based on logical puzzles and maze solving. The system covers hardware-accelerated training loops and parameter-efficient network design to reduce computational overhead while maintaining reasoning capabilities. - [conardli/easy-dataset](https://awesome-repositories.com/repository/conardli-easy-dataset.md) (13,394 ⭐) — Easy-dataset is a comprehensive platform designed for the end-to-end management of machine learning datasets, specifically tailored for language and vision model fine-tuning. It functions as a centralized environment for the entire data lifecycle, encompassing the automated generation of synthetic training data, the structural organization of document collections, and the systematic annotation of individual data points. The platform distinguishes itself through its integrated evaluation and orchestration capabilities. It provides a dedicated suite for benchmarking models, featuring blind side-by-side human testing and automated grading to ensure objective performance metrics. Users can orchestrate complex data pipelines that transform raw documents into structured formats through recursive segmentation, automated taxonomy classification, and customizable text refinement. Beyond core generation and management, the system supports a wide range of data processing tasks, including visual document extraction, content augmentation, and the creation of multi-turn conversational datasets. It offers flexible configuration for model connections and generation parameters, allowing for fine-grained control over output quality and consistency. The platform is designed for local deployment to maintain data privacy and security. It includes built-in tools for programmatic quality assessment and supports the export of processed datasets into standard formats compatible with various fine-tuning pipelines. - [tatsu-lab/stanford_alpaca](https://awesome-repositories.com/repository/tatsu-lab-stanford-alpaca.md) (30,266 ⭐) — 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. - [microsoft/generative-ai-for-beginners](https://awesome-repositories.com/repository/microsoft-generative-ai-for-beginners.md) (112,045 ⭐) — This project is a comprehensive, open-source educational curriculum designed to guide developers through the mastery of generative artificial intelligence. It provides a structured learning path that covers foundational concepts, prompt engineering, and the practical application of large language models. The repository serves as a central hub for skill acquisition, offering sequential modules that progress from basic model mechanics to advanced architectural patterns. The curriculum distinguishes itself by focusing on the end-to-end lifecycle of intelligent software, including the implementation of retrieval-augmented generation and agentic workflow orchestration. It provides technical guidance on integrating diverse models—ranging from open-source options to cloud-based services—while emphasizing responsible development through systematic safety guardrails and ethical design practices. Learners are equipped to build functional applications, such as conversational interfaces, semantic search tools, and automated content generators, using standardized interfaces and modern development techniques. Beyond core model implementation, the resource covers operational practices for monitoring and maintaining AI systems in production. It includes practical modules on fine-tuning, vector-based indexing, and designing intuitive user experiences for intelligent systems. The repository is structured to support developers through every stage of the process, from initial environment configuration and dependency management to deployment readiness and troubleshooting. - [chiphuyen/aie-book](https://awesome-repositories.com/repository/chiphuyen-aie-book.md) (13,779 ⭐) — This project serves as a comprehensive educational resource and technical handbook for engineers building applications powered by large language models. It provides a structured framework for mastering the principles of artificial intelligence engineering, covering the full lifecycle of model development from initial design to production deployment. The repository distinguishes itself by offering a deep dive into the practical implementation of advanced design patterns, including retrieval-augmented generation, agentic tool orchestration, and parameter-efficient model adaptation. It emphasizes the importance of rigorous system evaluation, providing methodologies for assessing model reliability, monitoring health, and mitigating risks such as adversarial prompt injections. Beyond core engineering patterns, the content addresses the broader operational requirements of production-ready systems. This includes techniques for optimizing inference latency, curating synthetic training datasets, and designing robust prompt templates. The material is organized to support developers through real-world case studies, community-contributed study notes, and technical documentation that bridges the gap between theoretical concepts and applied software engineering. - [instruction-tuning-with-gpt-4/gpt-4-llm](https://awesome-repositories.com/repository/instruction-tuning-with-gpt-4-gpt-4-llm.md) (4,335 ⭐) — Instruction Tuning with GPT-4 - [apple/corenet](https://awesome-repositories.com/repository/apple-corenet.md) (6,999 ⭐) — Corenet is a deep learning training framework and computer vision model library designed for developing neural networks across vision, text, and audio modalities. It functions as a distributed training orchestrator for scaling workloads across multiple compute nodes and provides a multimodal data pipeline for processing image, text, and video data. The project includes a model conversion toolkit for transforming weights and architectures between different machine learning frameworks. It also provides tools for optimizing model performance on Apple Silicon and reducing response latency in generative models. The framework covers a broad range of capabilities, including visual recognition tasks such as object detection, semantic segmentation, and image classification. It supports advanced training techniques such as parameter-efficient fine-tuning, contrastive language-image pre-training, and structural reparameterization. Training and evaluation pipelines are managed through YAML-based configuration files and recipes to ensure reproducibility across environments. - [cloneofsimo/lora](https://awesome-repositories.com/repository/cloneofsimo-lora.md) (7,541 ⭐) — This project is a toolkit for fine-tuning and managing text-to-image diffusion models. It focuses on low-rank adaptation to create small, portable weight files that customize model styles and behaviors without modifying the entire base model. The project provides specialized utilities for model distillation using singular value decomposition to extract adapters from fully trained models, as well as tools for blending and merging multiple adapters through weight interpolation. It includes capabilities for subject inversion and pivotal tuning to increase the visual fidelity of specific identities. Additional capabilities cover the transformation of model weights between different storage formats for cross-engine compatibility. The toolkit also supports training for image inpainting and the co-training of text encoders to improve the association between specific tokens and visual concepts. - [paddlepaddle/paddlenlp](https://awesome-repositories.com/repository/paddlepaddle-paddlenlp.md) (12,953 ⭐) — PaddleNLP is a development library and toolkit for training, fine-tuning, and deploying large and small language models using the PaddlePaddle framework. It provides a comprehensive suite for the entire natural language processing lifecycle, from model development to high-performance inference. The project features a standardized model zoo for loading and managing pre-trained models and tokenizers through a unified interface. It distinguishes itself with a specialized model compression framework that reduces memory footprints via weight precision conversion and lossless size optimization, alongside an inference engine that utilizes operator fusion and backend-agnostic execution to increase token generation speed. The library covers a broad range of capabilities including distributed parallel training, parameter-efficient fine-tuning, and model weight merging. It also supports a full natural language processing pipeline for tasks such as text generation and zero-shot structured information extraction. - [microsoft/onnxruntime](https://awesome-repositories.com/repository/microsoft-onnxruntime.md) (19,347 ⭐) — This project is a cross-platform machine learning inference engine designed to execute pre-trained models across diverse operating systems and hardware environments. It functions as a standardized execution framework that manages the entire lifecycle of model inference, from loading and graph optimization to hardware-accelerated execution and generative sequence management. The runtime distinguishes itself through a highly modular architecture that decouples model logic from hardware-specific kernels. By utilizing an execution provider abstraction, it enables developers to offload computations to specialized hardware such as GPUs, NPUs, and dedicated chipsets. It also provides a comprehensive toolkit for model optimization, including quantization, precision conversion, and graph-level transformations, which allow for significant reductions in binary size and latency for both edge and cloud deployments. Beyond core inference, the project includes extensive support for generative AI, offering built-in capabilities for tokenization, chat template formatting, and streaming output generation. It supports complex model architectures through custom operator registration and modular adapter management, ensuring that developers can integrate specialized mathematical operations or fine-tuned model weights into their pipelines. The software is built primarily in C++ and provides language-specific bindings to facilitate integration into various programming environments. It includes robust diagnostic and profiling tools that allow for granular performance analysis, hardware utilization tracking, and debugging of tensor data during the inference process. - [martynwheeler/u-lora](https://awesome-repositories.com/repository/martynwheeler-u-lora.md) (0 ⭐) — This is a port of raspi-lora (https://pypi.org/project/raspi-lora/) for micropython. I have tested on raspberry pi pico, esp8266, and esp32. It allows your microcontroller to use an RFM95 radio to communicate. - [unslothai/unsloth](https://awesome-repositories.com/repository/unslothai-unsloth.md) (66,628 ⭐) — 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. - [wybiral/micropython-lora](https://awesome-repositories.com/repository/wybiral-micropython-lora.md) (0 ⭐) — MicroPython library for controlling a Semtech SX127x LoRa module over SPI. - [tloen/alpaca-lora](https://awesome-repositories.com/repository/tloen-alpaca-lora.md) (18,911 ⭐) — This project is a LLaMA fine-tuning framework and training pipeline designed for instruction tuning using low-rank adaptation. It provides a system for adapting large language models through a portable, containerized machine learning environment and a web-based inference interface. The framework enables the training of low-rank adapters and the subsequent merging of these weights back into base models to create standalone checkpoints. It includes utilities for defining and formatting prompt templates to ensure consistent data structures during the fine-tuning and inference processes. The project covers a broader set of capabilities including model weight export for external inference engines, token-based output streaming for real-time text generation, and containerized packaging of drivers and dependencies to ensure consistent execution across different hardware. - [ai4finance-foundation/fingpt](https://awesome-repositories.com/repository/ai4finance-foundation-fingpt.md) (20,507 ⭐) — 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. - [arendst/tasmota](https://awesome-repositories.com/repository/arendst-tasmota.md) (24,502 ⭐) — Tasmota is a universal firmware platform for ESP8266 and ESP32 microcontrollers, designed to provide local control and management of smart home hardware. It functions as an event-driven automation controller that replaces proprietary factory firmware, allowing users to manage relays, sensors, and lighting systems without relying on external cloud services. The system is built on a modular driver architecture that enables dynamic hardware configuration and peripheral support through a web-based management interface. The platform distinguishes itself through a template-driven hardware mapping system, which uses JSON strings to assign physical pins and drivers to specific device functions without requiring firmware recompilation. It acts as a multi-protocol gateway, bridging disparate standards like Zigbee, Bluetooth, LoRaWan, and Modbus into a unified network. By utilizing a local message-broker-based control model, Tasmota synchronizes device states and executes custom automation logic directly on the hardware, ensuring consistent operation even when disconnected from external controllers. Beyond its core bridging and control capabilities, the firmware includes a comprehensive suite of tools for system observability, data logging, and media management. It supports complex automation through a built-in rule engine, persistent flash-based filesystem storage for scripts and assets, and extensive integration options for major smart home ecosystems. The project provides a web-based provisioning interface for initial setup and supports remote firmware management to simplify the maintenance of distributed hardware fleets. - [datawhalechina/self-llm](https://awesome-repositories.com/repository/datawhalechina-self-llm.md) (30,941 ⭐) — 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. - [jiangpenghe/cl-lora](https://awesome-repositories.com/repository/jiangpenghe-cl-lora.md) (37 ⭐) — This repository contains the official PyTorch implementation of CL-LoRA: Continual Low-Rank Adaptation for Rehearsal-Free Class-Incremental Learning, accepted at CVPR 2025. - [d2l-ai/d2l-en](https://awesome-repositories.com/repository/d2l-ai-d2l-en.md) (29,001 ⭐) — 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. - [jianzhnie/llamatuner](https://awesome-repositories.com/repository/jianzhnie-llamatuner.md) (620 ⭐) — Easy and Efficient Finetuning LLMs. (Supported LLama, LLama2, LLama3, Qwen, Baichuan, GLM , Falcon) 大模型高效量化训练+部署. - [lich99/chatglm-finetune-lora](https://awesome-repositories.com/repository/lich99-chatglm-finetune-lora.md) (716 ⭐) — Code for fintune ChatGLM-6b using low-rank adaptation (LoRA)