--- title: "FastEdit vs KnowledgeEditingPapers" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hiyouga-fastedit-vs-zjunlp-knowledgeeditingpapers" tools: ["hiyouga-fastedit", "zjunlp-knowledgeeditingpapers"] --- # FastEdit vs KnowledgeEditingPapers *GraphCanon updated Aug 6, 2026* ## Verdict Pick FastEdit if fastEdit is a Python library for quick edits to large language models using PyTorch; pick KnowledgeEditingPapers if a specialized collection of foundational papers and reports that delve into the editing and manipulation of knowledge within large language models, making it a valuable resource for researchers looking to understand and斧. [FastEdit](https://github.com/hiyouga/FastEdit) reports 1.4k GitHub stars, 103 forks, and 21 open issues, last pushed Aug 13, 2023. [KnowledgeEditingPapers](https://github.com/zjunlp/KnowledgeEditingPapers) has 1.2k stars, 78 forks, and 0 open issues, last pushed Jun 25, 2026. Figures are from public GitHub metadata via [FastEdit's repository](https://github.com/hiyouga/FastEdit) and [KnowledgeEditingPapers's repository](https://github.com/zjunlp/KnowledgeEditingPapers). | | [FastEdit](/tools/hiyouga-fastedit.md) | [KnowledgeEditingPapers](/tools/zjunlp-knowledgeeditingpapers.md) | | --- | --- | --- | | Tagline | Editing large language models within 10 seconds | Must-read Papers on Knowledge Editing for Large Language Models | | Stars | 1,370 | 1,245 | | Forks | 103 | 78 | | Open issues | 21 | 0 | | Language | Python | - | | Adopt for | FastEdit is a Python library for quick edits to large language models using PyTorch. | A specialized collection of foundational papers and reports that delve into the editing and manipulation of knowledge within large language models, making it a valuable resource for researchers looking to understand and斧 | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | LLM Frameworks | LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [FastEdit](/tools/hiyouga-fastedit.md) | [KnowledgeEditingPapers](/tools/zjunlp-knowledgeeditingpapers.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Steady (60%) | | Days since push | 1086d | 42d | | Open issues (now) | 21 | 0 | | Owner type | User | Organization | | Full report | [trust report](/tools/hiyouga-fastedit/trust.md) | [trust report](/tools/zjunlp-knowledgeeditingpapers/trust.md) | ## 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. ## Decision facts: KnowledgeEditingPapers - **Hosting:** unknown - **Adopt for:** A specialized collection of foundational papers and reports that delve into the editing and manipulation of knowledge within large language models, making it a valuable resource for researchers looking to understand and斧 - **License detail:** MIT ## Choose when ### Choose FastEdit if… - License: FastEdit is Apache-2.0, KnowledgeEditingPapers 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. ### Choose KnowledgeEditingPapers if… - License: KnowledgeEditingPapers is MIT, FastEdit is Apache-2.0. - Tags unique to KnowledgeEditingPapers: knowledge-editing, model-editing, natural-language-processing, pre-trained-language-models. - Also covers Model Training. - You are specifically interested in recent advancements in knowledge editing techniques for large language models. ## 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. ## When NOT to use KnowledgeEditingPapers - You are looking for a broad overview of machine learning or AI in general, as this repository focuses narrowly on knowledge editing within large language models. - If you seek practical tooling or implementation guidance rather than theoretical insights and review papers. - Your focus is more on data preprocessing or model training techniques unrelated to the specific modification of knowledge mechanisms in LLMs. ## Common questions ### What is the difference between FastEdit and KnowledgeEditingPapers? FastEdit: Editing large language models within 10 seconds. KnowledgeEditingPapers: Must-read Papers on Knowledge Editing for Large Language Models. See the comparison table for live GitHub stats and shared categories. ### When should I choose FastEdit over KnowledgeEditingPapers? Choose FastEdit over KnowledgeEditingPapers when License: FastEdit is Apache-2.0, KnowledgeEditingPapers 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 choose KnowledgeEditingPapers over FastEdit? Choose KnowledgeEditingPapers over FastEdit when License: KnowledgeEditingPapers is MIT, FastEdit is Apache-2.0; Tags unique to KnowledgeEditingPapers: knowledge-editing, model-editing, natural-language-processing, pre-trained-language-models; Also covers Model Training; You are specifically interested in recent advancements in knowledge editing techniques for large language models. ### 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. ### When should I avoid KnowledgeEditingPapers? You are looking for a broad overview of machine learning or AI in general, as this repository focuses narrowly on knowledge editing within large language models. If you seek practical tooling or implementation guidance rather than theoretical insights and review papers. Your focus is more on data preprocessing or model training techniques unrelated to the specific modification of knowledge mechanisms in LLMs. ### Is FastEdit or KnowledgeEditingPapers more popular on GitHub? FastEdit has more GitHub stars (1,370 vs 1,245). Stars measure visibility, not whether either tool fits your constraints. ### Are FastEdit and KnowledgeEditingPapers open source? Yes - both are open-source projects on GitHub (FastEdit: Apache-2.0, KnowledgeEditingPapers: MIT). ### Where can I find alternatives to FastEdit or KnowledgeEditingPapers? GraphCanon lists graph-backed alternatives at [FastEdit alternatives](/tools/hiyouga-fastedit/alternatives) and [KnowledgeEditingPapers alternatives](/tools/zjunlp-knowledgeeditingpapers/alternatives) ([FastEdit markdown twin](/tools/hiyouga-fastedit/alternatives.md), [KnowledgeEditingPapers markdown twin](/tools/zjunlp-knowledgeeditingpapers/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/hiyouga-fastedit-vs-zjunlp-knowledgeeditingpapers.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, FastEdit or KnowledgeEditingPapers? FastEdit: Dormant. KnowledgeEditingPapers: Steady. 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 FastEdit and KnowledgeEditingPapers? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [FastEdit trust report](/tools/hiyouga-fastedit/trust); [KnowledgeEditingPapers trust report](/tools/zjunlp-knowledgeeditingpapers/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=hiyouga-fastedit`](/api/graphcanon/graph?tool=hiyouga-fastedit) - 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/_