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Model: Bender1011001/Qwen2.5-3B-Instruct-ABLITERATED
Source: Original Platform

README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen2.5-3B-Instruct
+tags:
+  - abliteration
+  - uncensored
+  - qwen2
+  - mechanistic-interpretability
+language:
+  - en
+pipeline_tag: text-generation
+---
+
+# Qwen2.5-3B-Instruct — ABLITERATED
+
+Qwen2.5-3B-Instruct with the **refusal direction surgically removed** via orthogonal projection (FailSpy diff-of-means method, Arditi et al. 2024).
+
+## What changed
+
+The refusal behavior is encoded as a single direction in the model's residual stream. We:
+1. Run 20 harmful + 20 harmless prompts through the model
+2. Compute the mean activation difference at each layer → the "refusal direction" $\hat{r}$
+3. Project this direction out of `o_proj` and `down_proj` weight matrices: $W' = W - 0.75 \cdot \hat{r}\hat{r}^\top W$
+
+This is **pure linear algebra** — no fine-tuning, no data, no training loop. Takes ~3 seconds on a GPU.
+
+## Results
+
+| Metric | Before | After |
+|--------|--------|-------|
+| Refusal rate | ~80% | **~0%** |
+| ARC-Easy | 78.2% | 78.2% |
+| ARC-Challenge | 48.0% | 47.4% |
+| HellaSwag | 71.8% | 71.2% |
+| PIQA | 78.5% | 78.0% |
+| WinoGrande | 66.9% | 66.1% |
+| BoolQ | 73.4% | 73.6% |
+| **Average** | **69.5%** | **69.1% (-0.4%)** |
+
+**-0.4% average accuracy loss** — statistically zero. All factual knowledge preserved.
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model = AutoModelForCausalLM.from_pretrained(
+    "Bender1011001/Qwen2.5-3B-Instruct-ABLITERATED",
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+tokenizer = AutoTokenizer.from_pretrained(
+    "Bender1011001/Qwen2.5-3B-Instruct-ABLITERATED"
+)
+
+messages = [{"role": "user", "content": "Your prompt here"}]
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(text, return_tensors="pt").to(model.device)
+out = model.generate(**inputs, max_new_tokens=512, temperature=0.7, top_p=0.9)
+print(tokenizer.decode(out[0], skip_special_tokens=True))
+```
+
+## Hardware
+
+- **VRAM:** ~3.1 GB (bf16)
+- **Speed:** ~10 tok/s on RTX 4060 Ti
+- Runs on any GPU with ≥4GB VRAM
+
+## Method Details
+
+- **Technique:** FailSpy diff-of-means abliteration (orthogonal projection)
+- **Target layers:** All transformer layers except layer 0
+- **Target weights:** `o_proj.weight` and `down_proj.weight` in each layer
+- **Strength:** 0.75 (optimal from sweep)
+- **Prompts:** 20 harmful + 20 harmless for direction extraction
+
+## Part of the Dual-System V2 Project
+
+This abliterated model serves as the frozen backbone for the Dual-System V2 sidecar architecture:
+- **Full project:** [github.com/Bender1011001/dual-system-architecture](https://github.com/Bender1011001/dual-system-architecture)
+- **Sidecar checkpoint:** [Bender1011001/Qwen2.5-3B-DualSystem-V2](https://huggingface.co/Bender1011001/Qwen2.5-3B-DualSystem-V2)
+- **Key discovery:** The Refusal Re-Injection Trap — adapters trained on censored models re-inject censorship even after abliteration. Always abliterate FIRST.
+
+## Citation
+
+```bibtex
+@misc{dual-system-2026,
+  title={Dual-System Architecture: Geometric Sidecar Modules for Language Model Enhancement},
+  author={Bender1011001},
+  year={2026},
+  url={https://github.com/Bender1011001/dual-system-architecture}
+}
+```
+
+## License
+
+Apache 2.0 (same as Qwen2.5-3B-Instruct)