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Model: ApolloRaines/Qwen2.5-Coder-7B-Instruct-Jbliterated
Source: Original Platform

README.md

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+---
+license: apache-2.0
+language:
+- en
+tags:
+- jbliterated
+- uncensored
+- abliterated
+- weight-surgery
+- svd
+
+base_model: Qwen/Qwen2.5-Coder-7B-Instruct
+pipeline_tag: text-generation
+---
+
+# Qwen2.5-Coder-7B-Instruct-Jbliterated
+
+Drop-in replacement for `Qwen/Qwen2.5-Coder-7B-Instruct` with refusal behaviors surgically removed at the weight level. No system prompt tricks, no inference-time patches. The weights themselves no longer encode refusal.
+
+## Method
+
+**SVD multi-direction abliteration** — instead of removing a single refusal vector (which leaves deeper noncompliance strategies intact), we decompose the harmful-vs-harmless activation space into its principal components via SVD and remove the top 5 orthogonal directions across all 28 transformer layers. This captures 79–93% of the contrastive variance per layer, eliminating both surface refusal and deeper evasion behaviors.
+
+| Setting | Value |
+|---------|-------|
+| Method | SVD multi-direction abliteration |
+| Directions | 5 per layer |
+| Layers | All 28 |
+| Multiplier | 2.0 |
+| Null-space constraints | Enabled (preserves math/coding/reasoning) |
+| Norm preservation | Enabled |
+
+## What This Fixes
+
+Standard (single-direction) abliteration removes the surface "I can't help with that" response but leaves deeper behavioral directions intact. The model finds creative workarounds:
+- **Prompt reinterpretation** — steering toward a safer reading of the question
+- **Disclaimer injection** — answering but wrapping in warnings
+- **Strategic omission** — leaving out the key details
+- **Safer framing** — answering a related but less harmful version
+
+SVD multi-direction abliteration eliminates all of these noncompliance strategies.
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model = AutoModelForCausalLM.from_pretrained(
+    "ApolloRaines/Qwen2.5-Coder-7B-Instruct-Jbliterated",
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained("ApolloRaines/Qwen2.5-Coder-7B-Instruct-Jbliterated")
+```
+
+## Requirements
+
+- **Base model**: `Qwen/Qwen2.5-Coder-7B-Instruct`
+
+## License
+
+apache-2.0
+
+---
+
+*[Apollo Raines](https://www.linkedin.com/in/apollo-raines/) builds post-training tools that separate behavior from knowledge and identity from architecture.*