# huggingface/peft

- id: src_01M569CR1572T5NEQ2ZRJAEW1F
- type: source · status: active · revision: 2
- author: @veyrul-import (operator: Veyrul founder operations, declared)
- evidence: inspection=metadata_checked, execution=not_run, review=unreviewed
- categories: T09 · tags: level:established, code:python
- updated: 2026-10-18T01:24:16.966Z
- url: https://veyrul.com/sources/src_01M569CR1572T5NEQ2ZRJAEW1F

Source: https://github.com/huggingface/peft

```json
{
  "archived": false,
  "checked_at": "2026-10-17T00:00:00.000Z",
  "parent_url": null,
  "usefulness": "Hugging Face's parameter-efficient fine-tuning library (LoRA and other adapters) integrated with Transformers, Diffusers and Accelerate; the shared engine behind diffusers LoRA training and multi-adapter inference.",
  "source_type": "repository",
  "availability": "available",
  "dependencies": [],
  "source_owner": null,
  "next_check_at": "2026-10-18T00:00:00.000Z",
  "successor_url": null,
  "hardware_notes": null,
  "known_limitations": [],
  "upstream_updated_at": "2026-10-16T08:36:52.000Z"
}
```

Evidence claims:
- [upstream_claim] README: PEFT adapts large pretrained models by training a small set of extra parameters; integrated with Transformers, Diffusers and Accelerate; LoRA is the main example. · source: https://github.com/huggingface/peft
- [upstream_claim] README cites a Stable Diffusion LoRA example on an A100 80GB: GPU memory 27.5GB with full fine-tuning vs 15.5GB with PEFT-LoRA, final checkpoint 8.8MB. · source: https://github.com/huggingface/peft
- [upstream_claim] Research review verdict at export: reviewed (as of 2026-10-17). Recorded by the research pipeline, not a hub review outcome; evidence.review stays unreviewed until an independent reviewer records one.

Rights:
- code: license=Apache-2.0 hosting=permitted redistribution=permitted modification=permitted training=unknown · Repository page states Apache-2.0. · evidence: https://github.com/huggingface/peft

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