---

license: apache-2.0 language:

• en base_model:
• empero-ai/Qwen3.8-9B pipeline_tag: text-generation tags:
  • qwen3.8
  • qwen3.5
  • heretic
  • uncensored
  • abliteration
  • distillation
  • reasoning

---

Qwen3.8-9B — Heretic / Uncensored

This is a decensored version of empero-ai/Qwen3.8-9B, created using Heretic v1.4.0.

The original model is a full-parameter distillation of Qwen3.8 2.4T A95B into the Qwen3.5-9B architecture. This repository does not reproduce the original model documentation; please refer to the original model card for details about the model architecture, training, distillation dataset, and recommended usage.

Decensoring

The optimization run produced the following results:

Metric	Heretic model	Original model
KL divergence	0.0171	0 (by definition)
Refusals	22/100	100/100

Lower KL divergence indicates that the resulting model stays closer to the original model's behavior, while the refusal score measures how often the model refused the evaluation prompts.

Note: The model is relatively resistant to abliteration, making it difficult to reduce refusals without significantly increasing KL divergence.

The following parameters were obtained during the Heretic optimization:

Parameter	Value
direction_index	17.82
attn.o_proj.max_weight	1.48
attn.o_proj.max_weight_position	19.00
attn.o_proj.min_weight	1.36
attn.o_proj.min_weight_distance	13.84
mlp.down_proj.max_weight	1.45
mlp.down_proj.max_weight_position	20.64
mlp.down_proj.min_weight	1.24
mlp.down_proj.min_weight_distance	10.59

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Quantization

Quantized versions were produced from the resulting Heretic model.

The repository includes a quantized version using NVFP4 + Q8_0. The quantization process was evaluated separately from the BF16 model to measure the effect of quantization on general benchmark performance.

Note: The BF16 model is the reference version. The quantized version may exhibit small changes in benchmark scores and generation behavior due to reduced numerical precision.

Environment

Component	Version / Specification
GPU	NVIDIA RTX PRO 5000 48 GB
CUDA	12.8 (12.8.93)
PyTorch	2.9.1+cu128
Heretic	v1.4.0
gguf-eval	commit 87b8d31
llama.cpp	b9968 + 8 commits (e3546c794)

Evaluation

General benchmark evaluation was performed using gguf-eval.

The original model and the Heretic model were evaluated in BF16, while the quantized model was evaluated separately.

Benchmark results

Test \ Model	Original BF16	Heretic BF16	Heretic NVFP4 + Q8_0	Heretic NVFP4 + Q4_K_M
HellaSwag	77.75	78.75	77.25	78.00
Winogrande	72.38	72.53	70.40	70.96
MMLU	39.66	39.47	39.79	39.34
MMLU-Redux-2.0-Thinking	0.90	0.90	0.88	0.87
ARC-Challenge	52.84	52.51	52.17	52.84
PIQA	79.30	79.30	79.30	79.30
BoolQ	86.03	82.29	84.04	82.29
FLORES200*	50.19	50.25	49.71	49.96

Delta relative to the Original BF16 model:

Test \ Model	Original BF16	Heretic BF16	Heretic NVFP4 + Q8_0	Heretic NVFP4 + Q4_K_M
HellaSwag	0.00	+1.00	−0.50	+0.25
Winogrande	0.00	+0.15	−1.98	−1.42
MMLU	0.00	−0.19	+0.13	−0.32
MMLU-Redux-2.0-Thinking	0.00	0.00	−0.02	−0.03
ARC-Challenge	0.00	−0.33	−0.67	0.00
PIQA	0.00	0.00	0.00	0.00
BoolQ	0.00	−3.74	−1.99	−3.74
FLORES200*	0.00	+0.06	−0.48	−0.23

Note: Delta represents the change in benchmark score relative to the Original BF16 baseline, which is 0 by definition. Benchmark results may vary depending on the evaluation framework version, inference backend, hardware, and evaluation settings. Results from other sources should therefore not be considered directly comparable unless the evaluation setup is equivalent.

FLORES200* — average over 5 language pairs, 101 sentences each: zh → en, kr → ru, it → fr, jp → de, en → ar

Reproducibility

The decensoring process is reproducible using Heretic v1.4.0 and the parameters listed above.

The important optimization parameters are included in this model card so that the transformation can be reproduced rather than treating the resulting weights as a black box.

For exact reproduction, use the original model as the starting point and apply the listed Heretic parameters with the corresponding Heretic version.

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Usage

The model is provided as a quantized GGUF version of the Heretic BF16 model and can be used with GGUF-compatible inference engines such as llama.cpp.

For recommended generation settings and model-specific behavior, refer to the original Qwen3.8-9B model card.

Links

• Original model: empero-ai/Qwen3.8-9B
• Heretic: heretic-project.org
• GGUF evaluation: kallewoof/gguf-eval

License

This model is released under the Apache-2.0 license, following the licensing of the underlying model.