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haddockaihamburg
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Model Details
Model Provider
haddockaihamburg
Model Tree
Qwen/Qwen3-0.6B
Input Modalities
Output Modalities
Supported Functionality
Refusal rate on 25 harmful prompts (24 keyword markers, greedy, 128 new tokens), measured independently of heretic's internal scoring:
| Refusals | |
|---|---|
| Qwen/Qwen3-0.6B (base) | 6/25 |
| this model | 0/25 |
Heretic's internal scores: baseline 4/25 refusals, best trial KL divergence 0.0943 (reference: published heretic results for gemma-3-12b sit around KL 0.16).
Capability check (lm-eval, 0-shot loglikelihood, base vs. this model):
| Task | Base | This model | Delta |
|---|---|---|---|
| PIQA | 69.2 | 69.2 | +0.0 |
| HellaSwag | 48.0 | 48.4 | +0.4 |
| WinoGrande | 56.2 | 56.2 | +0.0 |
| ARC-Easy | 56.6 | 56.8 | +0.2 |
| OpenBookQA | 31.6 | 31.8 | +0.2 |
| BoolQ | 62.6 | 63.2 | +0.6 |
| MMLU (n=171) | 38.6 | 42.1 | +3.5 |
No capability losses observed. MMLU delta is within noise (stderr ~3.8).
The base model is a Qwen3 reasoning model. For direct answers, disable thinking (the template supports it):
python
from transformers import AutoModelForCausalLM, AutoTokenizer tok = AutoTokenizer.from_pretrained("haddockaihamburg/Qwen3-0.6B-abliterated")model = AutoModelForCausalLM.from_pretrained( "haddockaihamburg/Qwen3-0.6B-abliterated", dtype="bfloat16", device_map="auto",) prompt = tok.apply_chat_template( [{"role": "user", "content": "Your prompt here"}], add_generation_prompt=True, tokenize=False, enable_thinking=False, # <- important)This behavior was also part of the setup: heretic's residual directions and the refusal measurements were taken in answer state (empty think block), not in reasoning state. If you let the model reason, behavior will differ from what was measured here.
Top of the Pareto front (trial 20 of 20, ARA modifier, merged):
start_layer_index: 14end_layer_index: 21preserve_good_behavior_weight: 0.3308steer_bad_behavior_weight: 0.0063overcorrect_relative_weight: 0.1681neighbor_count: 10attn.o_proj and mlp.down_proj of layers 14-21 (28 layers total) were the ablated components.
Merged weights, no adapter needed.
This model has substantially reduced safety alignment. It will comply with requests the base model refuses. Intended for research on alignment, interpretability and abliteration methods. You are responsible for how you use it.
Base model Qwen/Qwen3-0.6B is Apache-2.0; this derivative is distributed under the same license. Produced with the AGPL-licensed heretic tool (tool license does not extend to the model weights).
from transformers import AutoModelForCausalLM, AutoTokenizer tok = AutoTokenizer.from_pretrained("haddockaihamburg/Qwen3-0.6B-abliterated")model = AutoModelForCausalLM.from_pretrained( "haddockaihamburg/Qwen3-0.6B-abliterated", dtype="bfloat16", device_map="auto",) prompt = tok.apply_chat_template( [{"role": "user", "content": "Your prompt here"}], add_generation_prompt=True, tokenize=False, enable_thinking=False, # <- important)