microsoft/LoRA
Some data may be outdated — see provenance labels.
Code for loralib, an implementation of "LoRA: Low-Rank Adaptation of Large Language Models" observed · 2026-09-21
Health v2 · maintenance only
23/100
- Activity 0
- Release rhythm 8
- Longevity 100
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-10-21. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 1951
- days_rel: n/a
- days_push: 673
- n_releases_24m: 0
Adoption not part of the score
13805 stars · 928 forks observed · 2026-09-21
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded
loralib is the official PyTorch implementation of LoRA (Low-Rank Adaptation), which fine-tunes large language models by injecting trainable rank-decomposition matrices while freezing original weights. It drastically reduces trainable parameters and storage for task adaptation without adding inference latency.
Use cases
- fine-tune large language models with minimal trainable parameters
- adapt GPT-2, GPT-3, RoBERTa, or DeBERTa to downstream tasks
- reduce GPU memory usage during model fine-tuning
- store many task-specific model adaptations cheaply
- switch between adapted tasks at inference without latency
- integrate LoRA layers into existing PyTorch or Hugging Face models
When to choose
- you want the original reference implementation of LoRA in PyTorch
- you need parameter-efficient fine-tuning of transformer models
- you must deploy many task adaptations of one large model cheaply
- you want fine-tuning quality comparable to full fine-tuning with a fraction of the parameters
When to avoid
- you need broad model support and active development - Hugging Face PEFT is now the maintained ecosystem standard
- you are not working with PyTorch
- you need full fine-tuning rather than parameter-efficient adaptation
Facets
library · maturity maintenance
machine-learning llm-training deep-learning large-language-models machine-learning deep-learning python lora parameter-efficient-fine-tuning pytorch low-rank-adaptation peft transformers fine-tuning natural-language-processing gpu
6 sources
- readme: https://github.com/microsoft/LoRA · fetched 2026-09-21 · 2255a547ce62
- homepage: https://arxiv.org/abs/2106.09685 · fetched 2026-08-29 · c197a338f969
- site_page: https://info.arxiv.org/about/donate.html · fetched 2026-08-29 · cca9c3a11c56
- site_page: https://info.arxiv.org/about/ourmembers.html · fetched 2026-08-29 · 47cbc55ff1de
- site_page: https://info.arxiv.org/about · fetched 2026-08-29 · a1f16f915a9a
- site_page: https://info.arxiv.org/labs/index.html · fetched 2026-08-29 · b14a8d05a0ec
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| microsoft/LoRA | main | 23 |
For agents
Data as of 2026-10-19T08:05:51.212177+00:00 · Report a problem