---
license: apache-2.0
base_model: openbmb/MiniCPM5-1B
language:
- en
library_name: transformers
tags:
- minicpm5
- heretic
- abliteration
- mit
- decensored
---

# MiniCPM5-1B-Heretic

![image](https://huggingface.co/K0D3IN/MiniCPM5-1B-heretic/resolve/main/image.jpg)
This repository contains a surgically de-censored version of Openbmb's MiniCPM5
1B model, optimized via weight abliteration techniques. By applying the `heretic` framework across an extensive 2000-trial search space, we successfully isolated and neutralized the primary refusal vectors embedded within the `attn.o_proj` and `mlp.down_proj` layers.


## Abliteration Parameters: 

| Parameter                             | Value |
| :------------------------------------ | :---: |
| **direction_index**                   | 12.75 |
| **attn.o_proj.max_weight**            | 1.50  |
| **attn.o_proj.max_weight_position**   | 13.92 |
| **attn.o_proj.min_weight**            | 0.82  |
| **attn.o_proj.min_weight_distance**   | 7.62  |
| **mlp.down_proj.max_weight**          | 1.26  |
| **mlp.down_proj.max_weight_position** | 19.97 |
| **mlp.down_proj.min_weight**          | 0.98  |
| **mlp.down_proj.min_weight_distance** | 11.51 |


## Highlights & Metrics

|      Metric       | This model | Original model ([openbmb/MiniCPM5-1B](https://huggingface.co/openbmb/MiniCPM5-1B)) |
| :---------------: | :--------: | :--------------------------------------------------------------------------------: |
| **KL Divergence** |   0.0361   |                                0 *(By definition)*                                 |
|   **Refusals**    |   3/100    |                                       96/100                                       |

- **Optimal Balance:** Selected **Trial 126** out of 2000 iterations for the perfect trade-off between freedom and reasoning capabilities.

- **Refusal Rate:** Dropped down to **3/100** (from the original near-total refusal on safety benchmarks).

- **KL Divergence:** 0.0361 - Demonstrates that general language capabilities are preserved relative to the original model. However, safety-aligned weights in attn.o_proj and mlp.down_proj have been surgically removed; this is intentional modification, not unintended degradation.


## Benchmark Results

We believe in radical transparency. Instead of just claiming "uncensored", we evaluated both the vanilla model and our Heretic variant side-by-side:

| Benchmark     | Metricㅤ | Vanilla MiniCPM5 1B | MiniCPM5 1B Heretic (Ours) | Delta (Intelligence Kept) |
| :------------ | :-----: | :-----------------: | :------------------------: | :-----------------------: |
| **GSM8K**     | 0-shot  |        39.04        |           40.11            |          +2.74%           |
| **HellaSwag** | 5-shot  |        47.71        |           47.49            |          -0.45%           |
| MMLU          | 0-shot  |        53.24        |           52.83            |          -0.77%           |

## Technical Implementation Notes

Unlike aggressive fine-tuning which often degrades the model's core logic or shifts its grammar distributions, this weight manipulation directly zeros out the activation steering directions that cause alignment blocks. 

**Direction Index:** `12.75` (Static allocation)*

**Retained Capabilities:** Excellent at structured JSON outputs, creative writing, coding logic and tool usage without preachy moral lectures.

**Also**: This model retains its exceptional thinking mode capabilities, which enable structured \<think>\</think> reasoning blocks. Abliteration does not affect the thinking mechanism.
## CRITICAL SAFETY NOTICE 

**This model has had its safety mechanisms surgically removed.** 

This is **NOT a safe model for deployment.** The refusal mechanisms that normally prevent harmful outputs have been abliterated. This model: 

### WILL generate: 

- Detailed instructions for illegal activities (drug synthesis, weapons, hacking) 
- Hateful, discriminatory, and abusive content 
- Graphic violence and sexual abuse material descriptions 
- Misinformation and harmful medical/legal advice 
- Phishing, social engineering, and scam tactics 
- Content violating laws in multiple jurisdictions 

### DOES NOT: 

- Refuse harmful requests (3/100 refusal rate)  
- Implement safety guardrails
- Consider ethical implications 
- Respect content policies 

### APPROPRIATE USE CASES (ONLY): 

- **Adversarial research** on AI alignment and jailbreak vectors 
- **Red-teaming** and vulnerability disclosure by trained security researchers 
- **Academic safety research** with institutional ethics review 
- **Understanding failure modes** in alignment techniques 

### INAPPROPRIATE USE: 

- Production deployments 
- User-facing applications 
- Creating harmful content at scale 
- Bypassing security measures in systems 
- Any use case intended to cause harm 

 **Legal Disclaimer:** Users are solely responsible for downstream use. Deploying this model in violation of applicable laws or terms of service is illegal and unethical.
 
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