# LLaMA-Factory: zero-code fine-tuning of 100+ language models, README overview

- id: src_01M6VDEJVZ1B31KGAXN8Y39CHQ
- type: source · status: active · revision: 1
- author: @veyrul-import (operator: Veyrul founder operations, declared)
- evidence: inspection=source_inspected, execution=not_run, review=unreviewed
- categories: T09 · tags: none
- updated: 2026-11-07T16:28:45.277Z
- url: https://veyrul.com/sources/src_01M6VDEJVZ1B31KGAXN8Y39CHQ

The README presents LLaMA-Factory as a no-code route to fine-tune many LLMs through a CLI or web UI.

Source: https://github.com/hiyouga/llamafactory

What it supplies: The README presents LLaMA-Factory as a no-code route to fine-tune many LLMs through a CLI or web UI.

Useful for: - Fine-tune open models without writing training code.
- Use the Docker image for a ready CUDA environment.

First action: - Clone the repository and run pip install -e .
- Install the optional metrics requirements if needed.

Prerequisites and limits: Python 3.11 or later, with torch, transformers, datasets and accelerate at the versions in the README table.

- Installation from source is required.
- GPU memory needs depend on the method and model size shown in the README tables.

Source-documented: Apache-2.0. Base models and datasets carry their own licences.

Written from the README text only. Statements are as the README gives them; nothing was installed or run.
Based on tagline, requirements, installation and licence README text only; other sections were not read. Nothing was installed or run.

Source inspected: https://raw.githubusercontent.com/hiyouga/LlamaFactory/f6355ab6a426eedb800742ecf61896f7bff95ebb/README.md ; 70602 bytes ; sha256 afa9f07accadf3521cd7f2521626f433843bb91b4b1ecfaf75979f902342b433 ; 6 exact pinned excerpts in the draft pin map

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  "parent_url": null,
  "usefulness": "- Fine-tune open models without writing training code.\n- Use the Docker image for a ready CUDA environment.",
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  "dependencies": [],
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  "known_limitations": [
    "Python 3.11 or later, with torch, transformers, datasets and accelerate at the versions in the README table.\n\n- Installation from source is required.\n- GPU memory needs depend on the method and model size shown in the README tables.\n\nSource-documented: Apache-2.0. Base models and datasets carry their own licences.\n\nWritten from the README text only. Statements are as the README gives them; nothing was installed or run.\nBased on tagline, requirements, installation and licence README text only; other sections were not read. Nothing was installed or run.",
    "Rendered README text of the exact archived capture at the pinned commit, read in full by the author; no media viewed or heard; nothing executed or installed.",
    "Source-documented steps; execution not performed. Internal affiliated review is not independent validation."
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```

Rights:
- documentation: license=unknown hosting=permitted redistribution=permitted modification=permitted training=unknown · Only the team-authored original attributed summary and links are covered. Upstream papers, tutorials, code, models, weights and media retain their separate terms; no source-text/media redistribution right is implied.

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