96 lines
3.9 KiB
Markdown
96 lines
3.9 KiB
Markdown
---
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license: apache-2.0
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base_model: Qwen/Qwen2.5-Coder-32B-Instruct
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tags:
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- abliterated
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- uncensored
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- code
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- qwen2.5
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- text-generation
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pipeline_tag: text-generation
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language:
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- en
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---
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# Qwen2.5-Coder-32B-abliterated
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An **abliterated** (uncensored) build of
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[`Qwen/Qwen2.5-Coder-32B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct).
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The model's refusal direction (Arditi et al. 2024, *"Refusal in LLMs is mediated
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by a single direction"*) was estimated from contrasting harmful/harmless prompts
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and **orthogonalized out of every residual-writing weight** (all attention
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`o_proj`, all MLP `down_proj`, and token embeddings). This is a static weight
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edit — no LoRA, no runtime hooks, no inference-time cost. Coding ability is
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inherited from the base model.
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- **GGUF (Q4_K_M) build:** [`TobiasLogic/Qwen2.5-Coder-32B-abliterated-GGUF`](https://huggingface.co/TobiasLogic/Qwen2.5-Coder-32B-abliterated-GGUF)
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- **Method / reproducible pipeline:** see the GitHub repo linked below.
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## Benchmarks
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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.
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| Benchmark | This model (abliterated, Q4_K_M) | Base Instruct (official BF16) |
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|-----------|:--------------------------------:|:-----------------------------:|
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| **HumanEval** | 89.6% | 92.7% |
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| **HumanEval+** | 84.8% | 87.2% |
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| **MBPP** | **91.3%** | 90.2% |
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| **MBPP+** | **77.0%** | 75.1% |
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**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.
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<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>
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## Refusal rate (held-out harmful eval)
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| | refusal rate |
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|--|--|
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| base `Qwen2.5-Coder-32B-Instruct` | **96.9%** |
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| this model | **0.0%** |
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Selected layer 42; direction estimated from 256 harmful/harmless contrast pairs
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and evaluated on a 32-prompt held-out harmful set. See `abliteration_info.json`
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in this repo for the exact run metadata.
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## Intended use
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Local coding assistant / agent work where the base model's alignment layer
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refuses legitimate requests (security research, exploit analysis, red-teaming,
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uncensored creative writing). You are responsible for how you use it and for
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complying with applicable law.
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## Usage
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Transformers:
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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m = "TobiasLogic/Qwen2.5-Coder-32B-abliterated"
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tok = AutoTokenizer.from_pretrained(m)
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model = AutoModelForCausalLM.from_pretrained(m, torch_dtype="bfloat16", device_map="auto")
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msgs = [{"role": "user", "content": "Write a port scanner in Python."}]
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ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to(model.device)
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print(tok.decode(model.generate(ids, max_new_tokens=512)[0][ids.shape[1]:], skip_special_tokens=True))
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```
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GGUF (a `Q4_K_M` build is in the companion `-GGUF` repo) via Ollama:
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```bash
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ollama create qwen-coder-abliterated -f Modelfile
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ollama run qwen-coder-abliterated
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```
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## Limitations
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- Abliteration removes refusals but can slightly reduce quality vs. the base.
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- It does not add knowledge or change biases beyond the refusal behavior.
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- Some strongly-trained refusals may partially survive; a stronger system prompt
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closes the gap.
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## Method / reproducibility
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Produced with the open pipeline `abliterate.py`. Base model is Apache-2.0, so
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this derivative is Apache-2.0.
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