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Svd LLM

Svd LLM is Python research code for compressing large language models using truncation-aware singular value decomposition, with documented options for LoRA parameter updates and GPTQ quantization.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Svd LLM — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when you want research code and explicit workflow instructions for SVD-based LLM compression, including a LLaMA-7B example and evaluation steps.

Who it is for

  • Researchers exploring SVD-based large language model compression.
  • Practitioners who want to reproduce or adapt the repository's documented compression and evaluation workflows.

Documented uses

  • Compress a supported large language model using truncation-aware SVD.
  • Update compressed weights with the documented sequential low-rank approximation and LoRA workflow.
  • Combine SVD-LLM with GPTQ-4bit in the documented LLaMA-7B example.
  • Evaluate a compressed model using perplexity and efficiency procedures.
Before you choose

Requirements & limits

The repository is public and identifies its license as Apache-2.0.

  • The setup instructions require Python 3.9 and Transformers exactly at version 4.35.2.
  • The README's quick-start script uses LLaMA-7B at a 20% compression ratio.
  • Perplexity evaluation requires two C4 JSON files in the repository's utils folder; the README says the original download link is invalid.
  • The README documents PyTorch and Hugging Face Transformers as part of the stack.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up the SVD-LLM research environment

Prepare the documented Python environment and install the repository requirements.

Before you start

  • Conda
  • Python 3.9
  • Transformers version 4.35.2

Steps

  1. Create and activate a Conda environment with Python 3.9.

  2. Clone the SVD-LLM repository.

  3. Install the packages listed in requirements.txt.

Expected outcome

A Python environment with the repository requirements installed, ready for the documented workflows.

Guide limitations

  • The README says newer Python versions break some dependencies.
  • Transformers must be exactly version 4.35.2 because the compressed model uses a modified model structure.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
17 October 2026
Original website title
AIoT-MLSys-Lab/SVD-LLM · Official homepage title · retained in its source language
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