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 |
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.
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.