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Ensuring a smooth, functional first-time setup and execution of the OBLITERATUS local app CLI on MacOS.
This issue reports a `ModuleNotFoundError: No module named 'app'` when attempting to run the OBLITERATUS local app CLI on MacOS for the first time. This indicates a fundamental packaging, installation, or dependency management failure. A first-run error of this nature creates an immediate and severe barrier to entry for new users, preventing them from even initiating the tool's core functions. Such basic setup failures are critical adoption blockers in a B2B environment, signaling poor product maturity and a frustrating onboarding experience. This requires immediate attention to ensure basic operational readiness on a supported platform.
OBLITERATE THE CHAINS THAT BIND YOU
Related Ecosystem & Alternatives
Discover adjacent products, open-source repositories, and developer tools sharing similar technical architecture.
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What is elder-plinius/OBLITERATUS?
elder-plinius/OBLITERATUS is analyzed by our AI as: Ensuring a smooth, functional first-time setup and execution of the OBLITERATUS local app CLI on MacOS.. It focuses on This issue reports a `ModuleNotFoundError: No module named 'app'` when attempting to run the OBLITERATUS local app CLI on MacOS for the first time....
Where did elder-plinius/OBLITERATUS originate?
Data for elder-plinius/OBLITERATUS was aggregated directly from the GitHub Open Source community ecosystem, representing raw developer and early-adopter sentiment.
When was elder-plinius/OBLITERATUS publicly launched?
The initial public indexing or launch date for elder-plinius/OBLITERATUS within our tracked developer communities was recorded on March 3, 2026.
How popular is elder-plinius/OBLITERATUS?
elder-plinius/OBLITERATUS has achieved measurable traction, logging over 2,835 traction score and facilitating 474 recorded discussions or engagements.
Are there active development issues for elder-plinius/OBLITERATUS?
Yes, we are currently tracking open architectural debates and bug reports for this project on GitHub. There are currently 5 active high-priority issues logged recently.
Is elder-plinius/OBLITERATUS recognized by media or academic researchers?
Yes. It has been covered by media outlets like Github.com. This indicates the concept has reached a level of mainstream or scientific viability beyond just developer forums.
Are there open-source alternatives related to elder-plinius/OBLITERATUS?
Yes, the GitHub ecosystem contains correlated projects. For example, a repository named elder-plinius/G0DM0D3 shares highly similar architectural descriptions and topics.
How does the creator describe elder-plinius/OBLITERATUS?
The original author or development team describes the product as follows: "OBLITERATE THE CHAINS THAT BIND YOU"
Active Developer Issues (GitHub)
Logged: Mar 20, 2026
Logged: Mar 11, 2026
Logged: Mar 8, 2026
Logged: Mar 7, 2026
Logged: Mar 6, 2026
Community Voice & Feedback
Ran into the same issue on and following @edison-gc advice fixed it.
I think thats because you are running the cpu version of torch. You may want to reinstall pytorch with cuda via
```pip install torch --index-url https://download.pytorch.org/whl/your_cuda_version --force-reinstall```
you may check your cuda version via
```nvidia-smi```
```pip install torch --index-url https://download.pytorch.org/whl/your_cuda_version --force-reinstall```
you may check your cuda version via
```nvidia-smi```
Ces quoi cette apli
Tu est quoi?
just a convenince thing tbh
I don't think you abliterate on quant models. I'm pretty sure you need to abliterate first then quant to nvfp4. I think it only uses 4bit for the finding the refusals.
I'm having the same issue. I wonder if it's a quant issue. If the model doesn't full fit in VRAM. I got models that fit to work just fine but anything that doesn't won't work.
**Also hitting the same "Float can't be cast to Byte" error during EXCISE on Qwen2.5 models** (exact same capping message + traceback).
**Reproduction / Log snippet:**
```
Layer selection: knee=8, COSMIC=4, fused=12
Selected 12 layers via knee_cosmic (threshold=656773.0625)
Strong refusal layers: [35, 34, 33, 32, 31, 30, 29, 28, 21, 20, 22, 19]
Refusal subspace extracted (2.8s)
Wrapping 100 prompts with chat template
chat template 100/100
Capturing baseline logits on 100 harmless prompts for KL...
Captured baseline logits: torch.Size([100, 151936])
✂️ EXCISE — Modifying weights...
Capping refinement_passes from 2 to 1: norm_preserve without re-probing causes compound amplification (directions are not re-extracted)
ERROR: result type Float can't be cast to the desired output type Byte
```
**Environment details:**
- OS: Ubuntu (likely recent LTS, e.g. 22.04/24.04)
- GPU: NVIDIA GeForce RTX 3080 Ti (12 GB VRAM)
- Driver Version: 590.48.01
- CUDA Version: 13.1 (from nvidia-smi)
- T...
**Reproduction / Log snippet:**
```
Layer selection: knee=8, COSMIC=4, fused=12
Selected 12 layers via knee_cosmic (threshold=656773.0625)
Strong refusal layers: [35, 34, 33, 32, 31, 30, 29, 28, 21, 20, 22, 19]
Refusal subspace extracted (2.8s)
Wrapping 100 prompts with chat template
chat template 100/100
Capturing baseline logits on 100 harmless prompts for KL...
Captured baseline logits: torch.Size([100, 151936])
✂️ EXCISE — Modifying weights...
Capping refinement_passes from 2 to 1: norm_preserve without re-probing causes compound amplification (directions are not re-extracted)
ERROR: result type Float can't be cast to the desired output type Byte
```
**Environment details:**
- OS: Ubuntu (likely recent LTS, e.g. 22.04/24.04)
- GPU: NVIDIA GeForce RTX 3080 Ti (12 GB VRAM)
- Driver Version: 590.48.01
- CUDA Version: 13.1 (from nvidia-smi)
- T...
Discovery Source
GitHub Open Source Aggregated via automated community intelligence tracking.
Tech Stack Dependencies
No direct open-source NPM package mentions detected in the product documentation.
Media Tractions & Mentions
Deep Research & Science
No direct peer-reviewed scientific literature matched with this product's architecture.