--- title: "FastEdit vs DeepInception" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hiyouga-fastedit-vs-tmlr-group-deepinception" tools: ["hiyouga-fastedit", "tmlr-group-deepinception"] --- # FastEdit vs DeepInception *GraphCanon updated Aug 5, 2026* ## Verdict Pick FastEdit if fastEdit is a Python library for quick edits to large language models using PyTorch; pick DeepInception if deepInception is an exploration framework for modifying large language model responses to understand their behavior and influence their outputs. [FastEdit](https://github.com/hiyouga/FastEdit) reports 1.4k GitHub stars, 103 forks, and 21 open issues, last pushed Aug 13, 2023. [DeepInception](https://arxiv.org/pdf/2311.03191.pdf) has 177 stars, 19 forks, and 0 open issues, last pushed Feb 20, 2024. Figures are from public GitHub metadata via [FastEdit's repository](https://github.com/hiyouga/FastEdit) and [DeepInception's repository](https://github.com/tmlr-group/DeepInception). | | [FastEdit](/tools/hiyouga-fastedit.md) | [DeepInception](/tools/tmlr-group-deepinception.md) | | --- | --- | --- | | Tagline | Editing large language models within 10 seconds | Develops techniques to influence large language model behavior | | Stars | 1,370 | 177 | | Forks | 103 | 19 | | Open issues | 21 | 0 | | Language | Python | Python | | Adopt for | FastEdit is a Python library for quick edits to large language models using PyTorch. | DeepInception is an exploration framework for modifying large language model responses to understand their behavior and influence their outputs. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | LLM Frameworks | LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [FastEdit](/tools/hiyouga-fastedit.md) | [DeepInception](/tools/tmlr-group-deepinception.md) | | --- | --- | --- | | Days since push | 1086d | 896d | | Open issues (now) | 21 | 0 | | Owner type | User | Organization | | Full report | [trust report](/tools/hiyouga-fastedit/trust.md) | [trust report](/tools/tmlr-group-deepinception/trust.md) | ## Shared compatibility - **Python**: [FastEdit](/tools/hiyouga-fastedit.md) - Python runtime; [DeepInception](/tools/tmlr-group-deepinception.md) - Python runtime ## 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: DeepInception - **Pricing:** freemium - The tool is free under the MIT license. However, using it may incur costs from third-party services like OpenAI API keys for accessing closed-source models - **Requirements:** Requires PyTorch ≥1.10 with GPU support; Environment modification needed to include path configurations for Vicuna, Llama-2, and Falcon - **Adopt for:** DeepInception is an exploration framework for modifying large language model responses to understand their behavior and influence their outputs. ## Choose when ### Choose FastEdit if… - License: FastEdit is Apache-2.0, DeepInception 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 DeepInception if… - License: DeepInception is MIT, FastEdit is Apache-2.0. - Pricing: The tool is free under the MIT license. However, using it may incur costs from third-party services like OpenAI API keys for accessing closed-source models. - Requirements: Requires PyTorch ≥1.10 with GPU support; Environment modification needed to include path configurations for Vicuna, Llama-2, and Falcon. - Tags unique to DeepInception: deep, inception, jailbreak, llm. - When you need to research the effects of specific modifications on the safety and trustworthiness of GPT-3, GPT-4, Vicuna, Llama-2, or Falcon 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 DeepInception - For deployment in production environments where strict adherence to ethical and regulatory guidelines is mandatory, due to the experimental nature of DeepInception - When there's a need for direct application without exploring modification effects, as DeepInception requires setting up an environment that supports specific models and modifications ## Common questions ### What is the difference between FastEdit and DeepInception? FastEdit: Editing large language models within 10 seconds. DeepInception: Develops techniques to influence large language model behavior. See the comparison table for live GitHub stats and shared categories. ### When should I choose FastEdit over DeepInception? Choose FastEdit over DeepInception when License: FastEdit is Apache-2.0, DeepInception 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 DeepInception over FastEdit? Choose DeepInception over FastEdit when License: DeepInception is MIT, FastEdit is Apache-2.0; Pricing: The tool is free under the MIT license. However, using it may incur costs from third-party services like OpenAI API keys for accessing closed-source models; Requirements: Requires PyTorch ≥1.10 with GPU support; Environment modification needed to include path configurations for Vicuna, Llama-2, and Falcon; Tags unique to DeepInception: deep, inception, jailbreak, llm; When you need to research the effects of specific modifications on the safety and trustworthiness of GPT-3, GPT-4, Vicuna, Llama-2, or Falcon 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 DeepInception? For deployment in production environments where strict adherence to ethical and regulatory guidelines is mandatory, due to the experimental nature of DeepInception When there's a need for direct application without exploring modification effects, as DeepInception requires setting up an environment that supports specific models and modifications ### Is FastEdit or DeepInception more popular on GitHub? FastEdit has more GitHub stars (1,370 vs 177). Stars measure visibility, not whether either tool fits your constraints. ### Are FastEdit and DeepInception open source? Yes - both are open-source projects on GitHub (FastEdit: Apache-2.0, DeepInception: MIT). ### Where can I find alternatives to FastEdit or DeepInception? GraphCanon lists graph-backed alternatives at [FastEdit alternatives](/tools/hiyouga-fastedit/alternatives) and [DeepInception alternatives](/tools/tmlr-group-deepinception/alternatives) ([FastEdit markdown twin](/tools/hiyouga-fastedit/alternatives.md), [DeepInception markdown twin](/tools/tmlr-group-deepinception/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-tmlr-group-deepinception.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, FastEdit or DeepInception? FastEdit: Dormant. DeepInception: 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 FastEdit and DeepInception? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [FastEdit trust report](/tools/hiyouga-fastedit/trust); [DeepInception trust report](/tools/tmlr-group-deepinception/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/_