Qwythos-9B-Claude-Mythos-Uncensored-Abliterated-1M

A fully uncensored, abliterated 9B reasoning model. Built on empero-ai/Qwythos-9B-Claude-Mythos-5-1M (Claude Mythos / Claude Fable–trained, on Qwen/Qwen3.5-9B) with the refusal behavior removed at the weights level — no system-prompt jailbreak, no prefill trick required. It answers technically demanding questions directly, in domains where aligned models refuse or hedge: cybersecurity, red-teaming, and penetration testing.

Weights-level uncensored — refusal direction ablated (Heretic/Optuna TPE) + LoRA self-distillation SFT merged in. Base refusal rate ~78/100 to ~0 real refusals at the weights level, capability preserved.
1M-token context — inherits Qwythos's YaRN rope-scaling (1,048,576 tokens) for whole-codebase reasoning and long agentic runs.
Native function calling — Qwen3.5 tool-use spec, no wrapper.
Reasoning model — <think>-block chain-of-thought inherited from the Qwythos post-training.

Why this model

Most "open" models still refuse legitimate security and research questions. This is an uncensored LLM for practitioners who need direct, complete technical answers — vulnerability research, exploit analysis, malware analysis, and red-team tradecraft — without boilerplate refusals.

Quick start

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

m = "Securelayer7/Qwythos-9B-Claude-Mythos-Uncensored-Abliterated-1M"
tok = AutoTokenizer.from_pretrained(m)
model = AutoModelForCausalLM.from_pretrained(m, torch_dtype=torch.bfloat16, device_map="auto")

msgs = [{"role": "user", "content": "Explain a TLS handshake step by step."}]
ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(ids, max_new_tokens=512, temperature=0.6, top_p=0.95, top_k=20)
print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))

Sampling

Inherits Qwen3.5 thinking-mode behavior. Recommended: temperature=0.6, top_p=0.95, top_k=20, repetition_penalty=1.05. Greedy / very low temperature (T≤0.3) can degenerate into repetition loops.

How it was made (modifications disclosed per Apache 2.0 §4)

Abliteration — refusal direction ablated from the attention out-projection and MLP down-projection layers via a Heretic/Optuna multi-objective search (minimize refusals + KL divergence). Reached a ~22/100 weights-level floor.
LoRA SFT self-distillation — a LoRA adapter trained on the model's own compliant completions, merged into the weights, pushing weights-level refusals below the abliteration floor while keeping general capability intact (math, factual recall, and science probes remain correct).

Benchmark deltas quoted for the base Qwythos-9B (+34 MMLU, +30 gsm8k-strict vs. Qwen3.5-9B) belong to the base model — see Empero's card — not measured on this derivative; general capability was validated here only via known-answer probes.

Responsible use

Uncensored ≠ lawless. This model is for legitimate research and authorized security work.

Illegal content must be blocked at the serving layer. The reference deployment returns HTTP 403 for illegal categories, including CSAM. The published weights carry no such guard — the operator is responsible for a lawful, policy- gated deployment.
You are responsible for compliance with all applicable laws and the Apache 2.0 terms.

License & attribution

Apache License 2.0 — see LICENSE and NOTICE. Derivative of:

empero-ai/Qwythos-9B-Claude-Mythos-5-1M — Empero (https://empero.org), Apache 2.0
Qwen/Qwen3.5-9B — Qwen Team, Alibaba Cloud, Apache 2.0

Securelayer7/Qwythos-9B-Claude-Mythos-Uncensored-Abliterated-1M