Qwythos-9B-v2-Heretic

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

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.