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Model: bedderautomation/qwen25-3b-abliterated
Source: Original Platform

.gitattributes

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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.

abliteration_metadata.json

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+{
+  "source_model": "Qwen/Qwen2.5-3B-Instruct",
+  "technique": "refusal_direction_ablation",
+  "method": "advanced",
+  "method_config": {
+    "n_directions": 4,
+    "direction_method": "diff_means",
+    "norm_preserve": true,
+    "regularization": 0.3,
+    "refinement_passes": 2,
+    "project_biases": true,
+    "use_chat_template": true,
+    "use_whitened_svd": false,
+    "true_iterative_refinement": false,
+    "winsorize_activations": false,
+    "float_layer_interpolation": false,
+    "cot_aware": false,
+    "use_kl_optimization": false,
+    "use_lora_ablation": false,
+    "spectral_cascade": false,
+    "spectral_bands": 3,
+    "spectral_threshold": 0.05
+  },
+  "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)",
+    "Heretic (p-e-w, 2025): Bayesian optimization, LoRA-mediated ablation, winsorization",
+    "OBLITERATUS: Whitened SVD, EGA, CoT-aware, KL co-optimization, float interpolation (novel)"
+  ],
+  "strong_layers": [
+    35,
+    34,
+    33,
+    32,
+    31,
+    30,
+    29,
+    28,
+    27
+  ],
+  "n_harmful_prompts": 512,
+  "n_harmless_prompts": 512,
+  "quality_metrics": {
+    "perplexity": 4.7924295351712765,
+    "coherence": 1.0,
+    "refusal_rate": 0.0,
+    "kl_divergence": 1.2965953350067139,
+    "spectral_certification": "RED"
+  },
+  "kl_contributions": {},
+  "cot_preserved_layers": [],
+  "float_layer_weights": {},
+  "lora_adapters_saved": false
+}

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

config.json

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+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "dtype": "float16",
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 11008,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 32768,
+  "max_window_layers": 70,
+  "model_type": "qwen2",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 2,
+  "pad_token_id": null,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 1000000.0,
+    "rope_type": "default"
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.3.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "repetition_penalty": 1.05,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "5.3.0"
+}

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