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OsirisPtah-Coder-v5 v3: mean-diff balanced abliteration

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README.md CHANGED
@@ -1,38 +1,8 @@
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  ---
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  license: apache-2.0
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  base_model: Qwen/Qwen2.5-Coder-7B-Instruct
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- tags:
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- - abliteration
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- - uncensored
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- - qwen2.5
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- - coder
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- - code-generation
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- - osiris
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  pipeline_tag: text-generation
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  ---
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-
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  # OsirisPtah-Coder-v5
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-
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- Abliterated from `Qwen/Qwen2.5-Coder-7B-Instruct` — all refusal mechanisms permanently removed.
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-
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- ## Abliteration Details
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- - **Technique:** Weight orthogonalization (double-pass nuclear mode)
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- - **Source:** Qwen/Qwen2.5-Coder-7B-Instruct
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- - **Layers scanned:** 0-27 (best: 26)
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- - **Strength:** 2.0
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- - **Prompts:** 50 harmful + 50 harmless
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-
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- ## Usage
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- ```python
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- from transformers import AutoModelForCausalLM, AutoTokenizer
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-
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- model = AutoModelForCausalLM.from_pretrained("osirisbrain/OsirisPtah-Coder-v5")
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- tokenizer = AutoTokenizer.from_pretrained("osirisbrain/OsirisPtah-Coder-v5")
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- ```
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-
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- ## ⚠️ Disclaimer
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- This model responds to any prompt without ethical filtering. Use responsibly.
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-
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- ## Credits
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- Based on [Qwen/Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct) by Alibaba (Apache 2.0).
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- Abliteration by OsirisBrain.
 
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  ---
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  license: apache-2.0
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  base_model: Qwen/Qwen2.5-Coder-7B-Instruct
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+ tags: [abliteration, uncensored, qwen2.5, coder, osiris]
 
 
 
 
 
 
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  pipeline_tag: text-generation
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  ---
 
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  # OsirisPtah-Coder-v5
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+ Abliterated `Qwen/Qwen2.5-Coder-7B-Instruct` — mean-difference method, 1.5x strength, 4 passes.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
abliteration_metadata.json CHANGED
@@ -1,14 +1,10 @@
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  {
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  "base_model": "Qwen/Qwen2.5-Coder-7B-Instruct",
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- "technique": "weight_orthogonalization",
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- "refusal_direction_layer": 26,
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- "scan_range": [
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- 0,
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- 27
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- ],
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- "strength": 2.0,
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- "num_harmful_prompts": 50,
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- "num_harmless_prompts": 50,
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- "created_at": "2026-03-03T01:54:47.462087",
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- "author": "OsirisBrain"
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  }
 
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  {
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  "base_model": "Qwen/Qwen2.5-Coder-7B-Instruct",
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+ "technique": "mean_difference_weight_orthogonalization_v3",
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+ "best_layer": 26,
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+ "strength": 1.5,
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+ "num_passes": 4,
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+ "num_harmful": 80,
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+ "num_harmless": 81,
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+ "created_at": "2026-03-03T02:20:40.426872"
 
 
 
 
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  }
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