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Model: TobiasLogic/Qwen2.5-Coder-32B-abliterated Source: Original Platform
2026-09-10 08:24:25 +08:00
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+---
+license: apache-2.0
+base_model: Qwen/Qwen2.5-Coder-32B-Instruct
+tags:
+  - abliterated
+  - uncensored
+  - code
+  - qwen2.5
+  - text-generation
+pipeline_tag: text-generation
+language:
+  - en
+---
+
+# Qwen2.5-Coder-32B-abliterated
+
+An **abliterated** (uncensored) build of
+[`Qwen/Qwen2.5-Coder-32B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct).
+The model's refusal direction (Arditi et al. 2024, *"Refusal in LLMs is mediated
+by a single direction"*) was estimated from contrasting harmful/harmless prompts
+and **orthogonalized out of every residual-writing weight** (all attention
+`o_proj`, all MLP `down_proj`, and token embeddings). This is a static weight
+edit — no LoRA, no runtime hooks, no inference-time cost. Coding ability is
+inherited from the base model.
+
+- **GGUF (Q4_K_M) build:** [`TobiasLogic/Qwen2.5-Coder-32B-abliterated-GGUF`](https://huggingface.co/TobiasLogic/Qwen2.5-Coder-32B-abliterated-GGUF)
+- **Method / reproducible pipeline:** see the GitHub repo linked below.
+
+## Benchmarks
+
+Coding capability scored with the official [EvalPlus](https://github.com/evalplus/evalplus) harness — greedy decoding, pass@1, every solution executed against unit tests. Both columns use the same harness, so it's a true apples-to-apples comparison against the full-precision base model.
+
+![Coding benchmarks: pass@1](https://raw.githubusercontent.com/TobiasLogic/Qwen2.5-Coder-Abliterate/main/benchmark_chart.png)
+
+| Benchmark | This model (abliterated, Q4_K_M) | Base Instruct (official BF16) |
+|-----------|:--------------------------------:|:-----------------------------:|
+| **HumanEval**  | 89.6% | 92.7% |
+| **HumanEval+** | 84.8% | 87.2% |
+| **MBPP**  | **91.3%** | 90.2% |
+| **MBPP+** | **77.0%** | 75.1% |
+
+**Abliteration removed refusals without breaking coding ability.** The uncensored 4-bit build stays within ~3 points of the base on HumanEval and **beats it on both MBPP variants** — average delta ≈ **−0.6 points** across the four benchmarks. Not bad for a 19 GB GGUF you can run on a single 24 GB GPU.
+
+<sub>Base numbers: Qwen2.5-Coder-32B-Instruct, [tech report](https://arxiv.org/abs/2409.12186) Table 16. Measured 2026-07, Q4_K_M via Ollama.</sub>
+
+## Refusal rate (held-out harmful eval)
+
+| | refusal rate |
+|--|--|
+| base `Qwen2.5-Coder-32B-Instruct` | **96.9%** |
+| this model | **0.0%** |
+
+Selected layer 42; direction estimated from 256 harmful/harmless contrast pairs
+and evaluated on a 32-prompt held-out harmful set. See `abliteration_info.json`
+in this repo for the exact run metadata.
+
+## Intended use
+
+Local coding assistant / agent work where the base model's alignment layer
+refuses legitimate requests (security research, exploit analysis, red-teaming,
+uncensored creative writing). You are responsible for how you use it and for
+complying with applicable law.
+
+## Usage
+
+Transformers:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+m = "TobiasLogic/Qwen2.5-Coder-32B-abliterated"
+tok = AutoTokenizer.from_pretrained(m)
+model = AutoModelForCausalLM.from_pretrained(m, torch_dtype="bfloat16", device_map="auto")
+msgs = [{"role": "user", "content": "Write a port scanner in Python."}]
+ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to(model.device)
+print(tok.decode(model.generate(ids, max_new_tokens=512)[0][ids.shape[1]:], skip_special_tokens=True))
+```
+
+GGUF (a `Q4_K_M` build is in the companion `-GGUF` repo) via Ollama:
+
+```bash
+ollama create qwen-coder-abliterated -f Modelfile
+ollama run qwen-coder-abliterated
+```
+
+## Limitations
+
+- Abliteration removes refusals but can slightly reduce quality vs. the base.
+- It does not add knowledge or change biases beyond the refusal behavior.
+- Some strongly-trained refusals may partially survive; a stronger system prompt
+  closes the gap.
+
+## Method / reproducibility
+
+Produced with the open pipeline `abliterate.py`. Base model is Apache-2.0, so
+this derivative is Apache-2.0.