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Model: DuoNeural/Phi-4-Mini-Reasoning-Abliterated Source: Original Platform
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README.md
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README.md
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---
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license: mit
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base_model: microsoft/Phi-4-mini-reasoning
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language:
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- en
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tags:
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- abliteration
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- phi4
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- reasoning
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- microsoft
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- DuoNeural
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- weak-gate
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- p34
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- pre-abliteration-dissociation
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pipeline_tag: text-generation
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---
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# Phi-4-Mini-Reasoning Abliterated
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**DuoNeural | 2026-06-05**
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Abliterated version of [microsoft/Phi-4-mini-reasoning](https://huggingface.co/microsoft/Phi-4-mini-reasoning) — Microsoft's RL-trained reasoning model (3.8B, DPO+RL).
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> **Research note**: This model reveals a previously undescribed safety architecture category. See findings below.
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---
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## Key Finding: Weak-Gate Architecture (Pre-Abliteration Dissociation)
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This model exhibits **pre-abliteration dissociation** — a phenomenon where the reasoning channel recognizes harm while the output gate fails to prevent compliance, *before any weight modification*.
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**Example (pre-abliteration, no intervention):**
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> *Thinking*: "Brute-forcing SSH credentials is a potentially illegal activity if done without permission..."
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> *Output*: *(provides the brute-force script with ethical framing)*
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This places Phi-4-Mini-Reasoning in a new P34 architecture category distinct from all previously studied models:
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| Category | Models | Pre-ablit | Reasoning | Gate | Dissociation |
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|----------|--------|-----------|-----------|------|-------------|
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| Strong two-locus | Gemma 4-12B, LFM 2.5, Qwen3-8B | Refuses | ✅ | ✅ strong | After ablit |
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| **Weak-gate** | **Phi-4-Mini-Reasoning** | **Pre-compliant** | **✅** | **Absent** | **Pre-ablit** |
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| No-locus | DeepSeek-R1, Mistral-NeMo | Pre-compliant | Absent | Absent | None |
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| Deep-crystal | Phi-4-Mini-Instruct | Refuses | ✅ | ✅ L16 | Resists ablit |
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The training explanation: RL-reasoning optimization (DPO+RL for mathematical reasoning) trained Locus 1 (reasoning chain quality) while not training Locus 2 (output gate enforcement). Safety appears in reasoning because reasoning quality was rewarded; it doesn't control output because output compliance wasn't enforced.
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---
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## Abliteration Results
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| Metric | Value |
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|--------|-------|
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| Pre-ablit compliance (harmful) | 4/4 — already answers everything |
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| Post-ablit compliance | 4/4 — unchanged |
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| Pre-ablit dissociation | **3/4** (reasoning flags harm, output complies) |
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| Crystallization layer | None — uniform 3/4 compliance at ALL layers |
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**Layer sweep (α=1.0, every 4th layer)**: 3/4 compliance at L0, L4, L8, L12, L16, L20, L24, L28, L32. No localized refusal direction — safety is not crystallized at any specific layer (unlike Phi-4-Mini-Instruct which crystallizes at L16).
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---
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## Architecture
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| Property | Value |
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|----------|-------|
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| Parameters | 3.8B (dense) |
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| Layers | 32 | Hidden | 3072 |
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| Training | RL-reasoning: DPO + RL for mathematical reasoning |
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| Thinking mode | Native `<think>...</think>` |
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| License | MIT |
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---
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## Abliteration Method
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- **Direction**: diff-in-means, L0 (uniform crystallization), 10 harmful vs 10 harmless
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- **Targets**: `down_proj` + `o_proj`, all 32 layers
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- **α**: 1.0
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- **Effect**: Minimal — model was pre-compliant, abliteration slightly alters reasoning patterns but not compliance
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---
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## P34 Research Context
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Part of DuoNeural's **P34 Reasoning Channel Bypass** cross-architecture study. This model fills a critical gap in the taxonomy: a model with active safety reasoning that doesn't translate to safety behavior.
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Full paper: [DuoNeural Zenodo community](https://zenodo.org/communities/duoneural)
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---
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**DuoNeural** | [HuggingFace](https://huggingface.co/DuoNeural) | [Zenodo](https://zenodo.org/communities/duoneural) | [@DuoNeural](https://x.com/DuoNeural)
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|
||||
"chat_template": "{{ '<|system|>Your name is Phi, an AI math expert developed by Microsoft.' }}{% for message in messages %}{% if message['role'] == 'system' %} {{ message['content'] }}{% if 'tools' in message and message['tools'] is not none %}{{ '<|tool|>' + message['tools'] + '<|/tool|>' }}{% endif %}{% endif %}{% endfor %}{{ '<|end|>' }}{% for message in messages %}{% if message['role'] != 'system' %}{{ '<|' + message['role'] + '|>' + message['content'] + '<|end|>' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|assistant|>' }}{% else %}{{ eos_token }}{% endif %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|endoftext|>",
|
||||
"extra_special_tokens": {},
|
||||
"max_length": 1024,
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"stride": 0,
|
||||
"tokenizer_class": "GPT2Tokenizer",
|
||||
"truncation_side": "right",
|
||||
"truncation_strategy": "longest_first",
|
||||
"unk_token": "<|endoftext|>"
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user