lyogavin/airllm

Features

• Layer-Decomposition Engines - Executes massive language models on consumer hardware by decomposing layers and managing memory usage during inference.
• Memory-Constrained Inference - Runs massive language models on consumer hardware by optimizing memory usage to fit within the constraints of available graphics or system memory.
• Cross-Platform Inference Frameworks - Offers a unified interface for deploying and running large language models across Linux and Apple Silicon desktop environments.
• Large Language Model Fine-Tuning Frameworks - Provides a platform for fine-tuning and aligning large language models on limited hardware using low-rank adaptation and memory-efficient training methods.
• Apple Hardware Acceleration - Executes models on local desktop environments by leveraging native hardware acceleration libraries designed for specific processor architectures.
• Layer Decomposition Engines - Loads individual model layers into memory sequentially during inference to allow massive models to run on hardware with limited capacity.
• Long Context Training Optimizations - Analyzes and answers questions based on massive text inputs up to 100,000 tokens by utilizing memory-efficient sequence processing techniques.
• Large Language Model Optimization - Executes massive language models on consumer hardware by decomposing model layers and managing memory usage to fit within limited video memory capacity.
• Model Fine-Tuning - Provides tools for memory-efficient fine-tuning of pre-trained models on consumer-grade hardware.
• Memory Optimization - Reduces VRAM usage for large models using attention optimizations and parameter-efficient techniques to enable execution on consumer hardware.
• Quantization Toolkits - Reduces the memory footprint of large models through block-wise quantization and efficient layer loading techniques.
• Long-Context Sequence Processors - Analyzes and answers questions based on massive text inputs up to 100,000 tokens by utilizing memory-efficient sequence processing techniques.
• Model Quantization - Applies block-wise weight quantization to decrease memory footprint and accelerate inference performance.
• Parameter Efficient Fine-Tuning - Updates only a small subset of model parameters during training to minimize memory usage while aligning models with specific preferences.
• Preference Optimization - Uses direct preference optimization to align model outputs with human preferences on limited hardware.
• Inference and Serving - Memory-optimized inference for large models on consumer hardware.
• Model Serving & Deployment - Optimizes memory usage for running large models on limited hardware.
• Preference-Based Model Alignments - Implements direct preference optimization to align language models with human preferences.
• Hardware Abstraction Layers - Provides a consistent interface that maps high-level operations to native acceleration libraries across different processor architectures and operating systems.
• Context Memory Optimizations - Reduces the memory overhead of long-context processing by applying efficient sequence management techniques to handle massive input token windows.
• Model Inference Deployment - Runs language models on both Linux and desktop hardware using a unified interface to simplify local model deployment across different operating systems.
• Inference Latency Optimizers - Prefetches model layers during inference to hide data transfer latency and improve execution speed.
• Model Loading - Detects model types automatically to simplify initialization without requiring manual class specification for different architectures.
• Asynchronous Prefetchers - Overlaps data transfer and computation by loading upcoming model layers into memory before they are needed to hide latency.

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