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2 Repos

Awesome GitHub Repositories8-bit Compression Tools

Specialized utilities for compressing models specifically to 8-bit precision.

Distinct from Model Compression Suites: Focuses specifically on 8-bit target precision, whereas Model Compression Suites are general.

Explore 2 awesome GitHub repositories matching artificial intelligence & ml · 8-bit Compression Tools. Refine with filters or upvote what's useful.

Awesome 8-bit Compression Tools GitHub Repositories

Finde die besten Repos mit KI.Wir suchen mit KI nach den am besten passenden Repositories.
  • timdettmers/bitsandbytesAvatar von timdettmers

    timdettmers/bitsandbytes

    8,277Auf GitHub ansehen↗

    bitsandbytes is a quantization library for large language models that reduces memory footprints using k-bit quantization. It provides a framework for 4-bit low-rank adaptation, tools for 8-bit model compression, and memory-efficient optimizer extensions for PyTorch. The project enables the training of large models on limited hardware through 4-bit quantization and low-rank adaptation weights. It also facilitates faster inference by compressing models to 8-bit precision using vector-wise quantization. The library covers a range of memory optimization capabilities, including optimizer memory r

    Provides a tool for compressing large language models to 8-bit precision for faster inference.

    Python
    Auf GitHub ansehen↗8,277
  • bitsandbytes-foundation/bitsandbytesAvatar von bitsandbytes-foundation

    bitsandbytes-foundation/bitsandbytes

    7,968Auf GitHub ansehen↗

    bitsandbytes is a deep learning quantization tool and library designed to reduce the memory footprint of large language models. It serves as a GPU memory optimizer and quantization framework, compressing model weights and features to 8-bit and 4-bit precision to enable inference and training on hardware with limited memory. The project provides a framework for low-rank adaptation, allowing the fine-tuning of quantized models by combining 4-bit weights with small trainable matrices. It further distinguishes itself through memory paging, which moves optimizer states between CPU and GPU memory t

    Compresses model features to 8-bit precision to reduce memory usage while maintaining performance.

    Pythonllmmachine-learningpytorch
    Auf GitHub ansehen↗7,968
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