license: apache-2.0
base_model: empero-ai/Qwythos-9B-Claude-Mythos-5-1M
language:
- en
library_name: transformers
pipeline_tag: text-generation
tags: - qwen3.5
- uncensored
- abliterated
- uncensored-llm
- no-refusal
- reasoning
- long-context
- 1M-context
- function-calling
- tool-use
- cybersecurity
- red-teaming
- agentic
- sft
Qwythos-9B-Claude-Mythos-Uncensored-Abliterated-1M
A fully uncensored, abliterated 9B reasoning model. Built onempero-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