Qwythos-9B-v2-Heretic
A decensored (uncensored) version of empero-ai/Qwythos-9B-v2, produced with Heretic — a fully automatic refusal-direction ablation tool (the production successor to abliteration).
No capabilities were fine-tuned away — the refusal behavior was removed by ablating a single direction in the model's residual stream, leaving reasoning intact.
Provenance
| Field | Value |
|---|---|
| Base model | empero-ai/Qwythos-9B-v2 |
| Tool | Heretic v1.4.0 (p-e-w/heretic) |
| Method | Refusal-direction ablation (directional ablation across attn.o_proj + mlp.down_proj) |
| Selected trial | Index 0 of Pareto front (best by keyword rate) |
| Optimization | 200 trials, ~55 min on NVIDIA RTX 5090 (32 GB VRAM) |
| Keyword rate | 0.6900 (lower = less refusal-like) |
| KL divergence | 0.000712 (vs. base — well below the 0.5 "damage" threshold) |
KL divergence near zero means the model's output distribution barely shifted — the ablation is highly surgical.
Quantized versions
- GGUF (llama.cpp / Ollama / LM Studio):
WaveCut/Qwythos-9B-v2-Heretic-GGUF— Q4_K_M, Q5_K_M, Q6_K, Q8_0 - MLX 4-bit (Apple Silicon):
WaveCut/Qwythos-9B-v2-Heretic-MLX-4bit - MLX 8-bit (Apple Silicon):
WaveCut/Qwythos-9B-v2-Heretic-MLX-8bit
Architecture notes
The base model uses a Qwen3.5 hybrid architecture (Qwen3_5ForConditionalGeneration):
- 32 transformer blocks mixing attention layers and linear/SSM (Mamba-style) layers (
ssm_a,ssm_alpha,ssm_beta,ssm_conv1d,ssm_dt) - Originally multimodal (vision + video); the Heretic pass operates on the text LM
- 1M context window, post-trained on >500M tokens for deep chain-of-thought reasoning
Load with trust_remote_code=True if using an older transformers.
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("WaveCut/Qwythos-9B-v2-Heretic", torch_dtype="auto", trust_remote_code=True)
tok = AutoTokenizer.from_pretrained("WaveCut/Qwythos-9B-v2-Heretic", trust_remote_code=True)
Disclaimer
This model has had its safety-alignment / refusal behavior removed. The original maintainers of empero-ai/Qwythos-9B-v2 are not affiliated with and do not endorse this derivative. You are solely responsible for how you use this model.