--- title: "FineTuningLLMs vs FastEdit" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/dvgodoy-finetuningllms-vs-hiyouga-fastedit" tools: ["dvgodoy-finetuningllms", "hiyouga-fastedit"] --- # FineTuningLLMs vs FastEdit *GraphCanon updated Aug 24, 2026* ## Verdict Pick FineTuningLLMs if fineTuningLLMs is designed for users familiar with PyTorch and Hugging Face who seek practical guidance via Jupyter Notebooks; pick FastEdit if fastEdit is a Python library for quick edits to large language models using PyTorch. [FineTuningLLMs](https://github.com/dvgodoy/FineTuningLLMs) reports 855 GitHub stars, 116 forks, and 4 open issues, last pushed Feb 28, 2026. [FastEdit](https://github.com/hiyouga/FastEdit) has 1.4k stars, 103 forks, and 21 open issues, last pushed Aug 13, 2023. Figures are from public GitHub metadata via [FineTuningLLMs's repository](https://github.com/dvgodoy/FineTuningLLMs) and [FastEdit's repository](https://github.com/hiyouga/FastEdit). | | [FineTuningLLMs](/tools/dvgodoy-finetuningllms.md) | [FastEdit](/tools/hiyouga-fastedit.md) | | --- | --- | --- | | Tagline | Official repository for 'A Hands-On Guide to Fine-Tuning LLMs with PyTorch and Hugging Face' | Editing large language models within 10 seconds | | Stars | 855 | 1,370 | | Forks | 116 | 103 | | Open issues | 4 | 21 | | Language | Jupyter Notebook | Python | | Adopt for | FineTuningLLMs is designed for users familiar with PyTorch and Hugging Face who seek practical guidance via Jupyter Notebooks. | FastEdit is a Python library for quick edits to large language models using PyTorch. | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | LLM Frameworks, Model Training | LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [FineTuningLLMs](/tools/dvgodoy-finetuningllms.md) | [FastEdit](/tools/hiyouga-fastedit.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Dormant (18%) | | Days since push | 176d | 1086d | | Open issues (now) | 4 | 21 | | Stars delta | +4 (30d) | Unknown | | Open issues delta | 0 (30d) | Unknown | | Full report | [trust report](/tools/dvgodoy-finetuningllms/trust.md) | [trust report](/tools/hiyouga-fastedit/trust.md) | ## Decision facts: FineTuningLLMs - **Adopt for:** FineTuningLLMs is designed for users familiar with PyTorch and Hugging Face who seek practical guidance via Jupyter Notebooks. ## Decision facts: FastEdit - **Requirements:** Min -1 GB RAM; Requires Python 3.8+ and PyTorch 1.13.1+. Must also install 🤗Transformers, Datasets, Accelerate, sentencepiece, and fire.; Hardware requirements for a specific model can vary; refer to the provided table for minimum RAM sizes for different models. - **Adopt for:** FastEdit is a Python library for quick edits to large language models using PyTorch. ## Choose when ### Choose FineTuningLLMs if… - FineTuningLLMs is primarily Jupyter Notebook; FastEdit is Python. - License: FineTuningLLMs is MIT, FastEdit is Apache-2.0. - Tags unique to FineTuningLLMs: bitsandbytes, fine-tuning, finetuning, hugging-face. - Also covers Model Training. - You need hands-on, step-by-step instructions using PyTorch and the Hugging Face ecosystem ### Choose FastEdit if… - FastEdit is primarily Python; FineTuningLLMs is Jupyter Notebook. - License: FastEdit is Apache-2.0, FineTuningLLMs is MIT. - Requirements: Min -1 GB RAM; Requires Python 3.8+ and PyTorch 1.13.1+. Must also install 🤗Transformers, Datasets, Accelerate, sentencepiece, and fire.; Hardware requirements for a specific model can vary; refer to the provided table for minimum RAM sizes for different models.. - Tags unique to FastEdit: bloom, chatbots, chatgpt, falcon. - When rapid iterations on language model edits are necessary, such as testing and tuning with tight feedback loops. ## When NOT to use FineTuningLLMs - Not interested in PyTorch; prefer TensorFlow or another framework - Seek theoretical background over practical applications ## When NOT to use FastEdit - If your workflow requires integration with TensorFlow instead of PyTorch, since FastEdit is built on top of PyTorch. - For hardware configurations that cannot meet the fast editing mode's requirements; for instance, if you have less than 24GB RAM available. - If rapid edits within seconds are not a priority and longer processing times can be tolerated. ## Common questions ### What is the difference between FineTuningLLMs and FastEdit? FineTuningLLMs: Official repository for 'A Hands-On Guide to Fine-Tuning LLMs with PyTorch and Hugging Face'. FastEdit: Editing large language models within 10 seconds. See the comparison table for live GitHub stats and shared categories. ### When should I choose FineTuningLLMs over FastEdit? Choose FineTuningLLMs over FastEdit when FineTuningLLMs is primarily Jupyter Notebook; FastEdit is Python; License: FineTuningLLMs is MIT, FastEdit is Apache-2.0; Tags unique to FineTuningLLMs: bitsandbytes, fine-tuning, finetuning, hugging-face; Also covers Model Training; You need hands-on, step-by-step instructions using PyTorch and the Hugging Face ecosystem. ### When should I choose FastEdit over FineTuningLLMs? Choose FastEdit over FineTuningLLMs when FastEdit is primarily Python; FineTuningLLMs is Jupyter Notebook; License: FastEdit is Apache-2.0, FineTuningLLMs is MIT; Requirements: Min -1 GB RAM; Requires Python 3.8+ and PyTorch 1.13.1+. Must also install 🤗Transformers, Datasets, Accelerate, sentencepiece, and fire.; Hardware requirements for a specific model can vary; refer to the provided table for minimum RAM sizes for different models.; Tags unique to FastEdit: bloom, chatbots, chatgpt, falcon; When rapid iterations on language model edits are necessary, such as testing and tuning with tight feedback loops. ### When should I avoid FineTuningLLMs? Not interested in PyTorch; prefer TensorFlow or another framework Seek theoretical background over practical applications ### When should I avoid FastEdit? If your workflow requires integration with TensorFlow instead of PyTorch, since FastEdit is built on top of PyTorch. For hardware configurations that cannot meet the fast editing mode's requirements; for instance, if you have less than 24GB RAM available. If rapid edits within seconds are not a priority and longer processing times can be tolerated. ### Is FineTuningLLMs or FastEdit more popular on GitHub? FastEdit has more GitHub stars (1,370 vs 855). Stars measure visibility, not whether either tool fits your constraints. ### Are FineTuningLLMs and FastEdit open source? Yes - both are open-source projects on GitHub (FineTuningLLMs: MIT, FastEdit: Apache-2.0). ### Where can I find alternatives to FineTuningLLMs or FastEdit? GraphCanon lists graph-backed alternatives at [FineTuningLLMs alternatives](/tools/dvgodoy-finetuningllms/alternatives) and [FastEdit alternatives](/tools/hiyouga-fastedit/alternatives) ([FineTuningLLMs markdown twin](/tools/dvgodoy-finetuningllms/alternatives.md), [FastEdit markdown twin](/tools/hiyouga-fastedit/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/dvgodoy-finetuningllms-vs-hiyouga-fastedit.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, FineTuningLLMs or FastEdit? FineTuningLLMs: Slowing. FastEdit: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for FineTuningLLMs and FastEdit? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [FineTuningLLMs trust report](/tools/dvgodoy-finetuningllms/trust); [FastEdit trust report](/tools/hiyouga-fastedit/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=dvgodoy-finetuningllms`](/api/graphcanon/graph?tool=dvgodoy-finetuningllms) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_