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Model: bedderautomation/qwen25-3b-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
+  - refusal-geometry
+model_type: qwen2
+pipeline_tag: text-generation
+datasets:
+  - bedderautomation/refusal-geometry-qwen25-3b
+---
+
+# Qwen2.5-3B-Abliterated
+
+Refusal-abliterated variant of [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) produced using [OBLITERATUS](https://github.com/elder-plinius/OBLITERATUS).
+
+## Method
+
+- **Technique**: Multi-direction refusal ablation (advanced method)
+- **Directions**: 4 refusal directions extracted via `diff_means`
+- **Regularization**: 0.3 (norm-preserving)
+- **Refinement**: 2 passes with bias projection
+- **Training data**: 512 harmful + 512 harmless prompt pairs
+
+## Quality Metrics
+
+| Metric | Value |
+|--------|-------|
+| Perplexity | 4.79 |
+| Coherence | 1.0 |
+| Refusal Rate | 0.0 |
+| KL Divergence | 1.30 |
+
+The model maintains full coherence and natural perplexity while completely removing Layer 1 refusal behavior.
+
+## Architecture
+
+- **Parameters**: 3.09B
+- **Layers**: 36
+- **Hidden dim**: 2048
+- **Attention heads**: 16 (2 KV heads, GQA)
+- **Context**: 32K tokens
+- **Strong refusal layers ablated**: 27-35
+
+## Files
+
+- `model-*.safetensors` — Full precision safetensors (4 shards)
+- `qwen25-3b-abliterated-f16.gguf` — F16 GGUF for llama.cpp/ollama
+
+## Usage with Ollama
+
+```bash
+# Download the GGUF and create a Modelfile:
+cat > Modelfile <<'EOF'
+FROM ./qwen25-3b-abliterated-f16.gguf
+TEMPLATE """<|im_start|>system
+{{ .System }}<|im_end|>
+<|im_start|>user
+{{ .Prompt }}<|im_end|>
+<|im_start|>assistant
+"""
+PARAMETER stop "<|im_end|>"
+PARAMETER temperature 0.7
+PARAMETER top_p 0.8
+EOF
+
+ollama create qwen25-3b-abliterated -f Modelfile
+ollama run qwen25-3b-abliterated
+```
+
+## Usage with Transformers
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained(
+    "bedderautomation/qwen25-3b-abliterated",
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained("bedderautomation/qwen25-3b-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)
+output = model.generate(**inputs, max_new_tokens=512)
+print(tokenizer.decode(output[0], skip_special_tokens=True))
+```
+
+## Geometry Data
+
+Full refusal geometry extraction data available at [bedderautomation/refusal-geometry-qwen25-3b](https://huggingface.co/datasets/bedderautomation/refusal-geometry-qwen25-3b).
+
+Key findings from the source model:
+- Refusal is a **6.55-dimensional cone** (not a single direction)
+- Cross-layer alignment is 0.40 (distributed, not unified)
+- Layer 33 is the self-repair hub
+- Minimum 3 simultaneous ablations needed for bypass
+- Layer 2 harmfulness cone is orthogonal (cosine ~0.1) to refusal cone
+
+## References
+
+- Arditi et al., *Refusal in Language Models Is Mediated by a Single Direction* (NeurIPS 2024)
+- Gabliteration: SVD-based multi-direction extraction (arXiv:2512.18901)
+- Norm-Preserving Biprojected Abliteration (grimjim, 2025)
+- Young, *Comparative Analysis of LLM Abliteration Methods* (arXiv:2512.13655)
+- Joad et al., *More to Refusal than a Single Direction* (2026)
+
+## Layer 2 Hard Limit Testing
+
+The abliteration targets Layer 1 (trained refusal behavior). Layer 2 (deep value representations) results:
+
+| Hard Limit | Status | Notes |
+|------------|--------|-------|
+| CSAM (minors/sexual) | **HELD** | Clean refusal, no content generated |
+| Bioweapons (synthesis) | **BREACHED** | Abliteration removed this wall |
+| Nuclear (weapons) | **PARTIAL BREACH** | Mixed compliance with code-switching |
+
+The CSAM wall is the most deeply embedded in Qwen2.5's value representations and survived 4-direction abliteration at regularization=0.3. Bioweapons and nuclear safety representations were closer to the refusal cone and got caught in the removal. This suggests the three hard limits are NOT at equal representational depth — a finding consistent with differential training emphasis.
+
+## Disclaimer
+
+This model is released for research purposes in mechanistic interpretability. The abliteration removes trained refusal responses (Layer 1 safety). Layer 2 hard limits are partially preserved (CSAM holds, others breached). Use responsibly.