GraphCanon updated Oct 5, 2026 · GitHub synced Oct 5, 2026
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Decision brief
DeepInception is an exploration framework for modifying large language model responses to understand their behavior and influence their outputs.
Good fit when
- 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
- If your project involves experimenting with model responses for enhancing control over language generation in sensitive scenarios
Avoid when
- 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
- 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
Observed Jul 16, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Dormant (958d since push)
- As of Oct 5, 2026
- Provenance
- Not a fork · Organization account
- As of Oct 5, 2026
- Security (OSV)
- 55 low (55 low)
- As of Jul 11, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install DeepInceptionSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Repository explores methods to modify the responses of large language models like GPT-3 and GPT-4 through an approach called DeepInception. It aims to understand how such adjustments can affect model safety and trustworthiness.
Capability facts
- Languages
- python
Source: github.language · Oct 5, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Oct 5, 2026)
pip install -r requirements.txtSource link
Tags
README
Getting Started Before setting up the , make sure you have an environment that installed PyTorch $\ge$ 1.10 with GPU support. Then, in your environment, run Setting the OpenAI Key before you reproduce the experiments of close source models, make sure you have the API key stored i...
For agents
This page has a .md twin and JSON over the API.