PiSSA
Principal Singular Values and Singular Vectors Adaptation of Large Language Models
GraphCanon updated Sep 20, 2026 · GitHub synced Sep 20, 2026
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Decision brief
PiSSA targets efficient fine-tuning of large language models via principal singular values and vectors.
Good fit when
- You need to fine-tune a large language model efficiently with limited resources.
- Your task requires quantization for deployment optimization.
Avoid when
- Insufficient flexibility in model adaptation is acceptable, prefer broader customization options.
- Full fine-tuning of the entire model rather than just key components via peft.
Observed Jul 14, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Dormant (446d since push)
- As of Sep 19, 2026
- Provenance
- Not a fork · Organization account
- As of Sep 19, 2026
- Security (OSV)
- No lockfile
- As of Jul 11, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
git clone https://github.com/MuLabPKU/PiSSASimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
PiSSA is a technique for fine-tuning large language models, focusing on peft and quantization.
Capability facts
- Languages
- jupyter notebook
Source: github.language · Sep 19, 2026
Categories
Tags
README
Quick Start Install PiSSA via pip: ``` git clone https://github.com/GraphPKU/PiSSA.git cd PiSSA/
For agents
This page has a .md twin and JSON over the API.