5 dépôts
Awesome GitHub RepositoriesParameter Adaptation Techniques
Methods for fine-tuning models by injecting trainable matrices into frozen layers.
Distinguishing note: Specifically covers LoRA and similar low-rank adaptation methods.
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Awesome Parameter Adaptation Techniques GitHub Repositories
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- zai-org/chatglm-6b
zai-org/ChatGLM-6B
41,039Voir sur GitHubChatGLM-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 w
Injects low-rank adaptation matrices into frozen model layers to enable efficient fine-tuning with minimal overhead.
Python - huggingface/peft
huggingface/peft
21,274Voir sur GitHubThis 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, allowin
Decomposes large model matrices into smaller low-rank matrices to enable fine-tuning with minimal trainable parameters.
Pythonadapterdiffusionfine-tuning - microsoft/lora
microsoft/LoRA
13,264Voir sur GitHubLoRA 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. Furt
Enables efficient model refinement by injecting trainable low-rank decomposition matrices into frozen neural network layers.
Pythonadaptationdebertadeep-learning - modelscope/diffsynth-studio
modelscope/DiffSynth-Studio
12,585Voir sur GitHubDiffSynth-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, inclu
Implements low-rank adaptation techniques to efficiently adjust large generative models to specific styles or datasets.
Python - alirezadir/machine-learning-interviews
alirezadir/Machine-Learning-Interviews
8,455Voir sur GitHubThis 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 l
Explains low-rank adaptation (LoRA) as a parameter-efficient fine-tuning technique.
Jupyter Notebookagenticaiai-agents
