Comparison
FastEdit vs DeepInception
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.
Markdown twin · FastEdit alternatives · DeepInception alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | FastEdit | DeepInception |
|---|---|---|
| Maintenance | Dormant (1086d since push) As of 3w · github_public_v1 | Dormant (896d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 3w · github_public_v1 | Not a fork · Organization account As of 2w · github_public_v1 |
| OSV dependency advisories | Published findings As of 1mo · osv@v1 | Published findings As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- FastEdit
- Editing large language models within 10 seconds
- DeepInception
- Develops techniques to influence large language model behavior
Stars
- FastEdit
- 1.4k
- DeepInception
- 177
Forks
- FastEdit
- 103
- DeepInception
- 19
Open issues
- FastEdit
- 21
- DeepInception
- 0
Language
- FastEdit
- Python
- DeepInception
- Python
Adopt for
- FastEdit
- FastEdit is a Python library for quick edits to large language models using PyTorch.
- DeepInception
- DeepInception is an exploration framework for modifying large language model responses to understand their behavior and influence their outputs.
Persona
- FastEdit
- -
- DeepInception
- -
Runtime
- FastEdit
- -
- DeepInception
- -
License
- FastEdit
- Apache-2.0
- DeepInception
- MIT
Last pushed
- FastEdit
- Aug 13, 2023
- DeepInception
- Feb 20, 2024
Categories
- FastEdit
- LLM Frameworks
- DeepInception
- LLM Frameworks
Trust and health
Days since push
- FastEdit
- 1086d
- DeepInception
- 896d
Open issues (now)
- FastEdit
- 21
- DeepInception
- 0
Owner type
- FastEdit
- User
- DeepInception
- Organization
Full report
- FastEdit
- Trust report
- DeepInception
- Trust report
Shared compatibility
- Python · FastEdit: Python runtime · DeepInception: Python runtime
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.
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.
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 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
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (hiyouga/FastEdit) · observed Aug 3, 2026
- GitHub forks (hiyouga/FastEdit) · observed Aug 3, 2026
- Last push (hiyouga/FastEdit) · observed Aug 13, 2023
- License file (Apache-2.0) · observed Aug 3, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (tmlr-group/DeepInception) · observed Aug 5, 2026
- GitHub forks (tmlr-group/DeepInception) · observed Aug 5, 2026
- Last push (tmlr-group/DeepInception) · observed Feb 20, 2024
- License file (MIT) · observed Aug 5, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: FastEdit 1.4k · DeepInception 177 (synced Aug 3, 2026).
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 and DeepInception alternatives (FastEdit markdown twin, DeepInception markdown twin), 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 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; DeepInception trust report.