An abliterated (uncensored) build of 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
Method / reproducible pipeline: see the GitHub repo linked below.

Benchmarks

Coding capability scored with the official 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.

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.

_{Base numbers: Qwen2.5-Coder-32B-Instruct, tech report Table 16. Measured 2026-07, Q4_K_M via Ollama.}

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.