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Cataloged from empero-ai/Qwythos-9B-Claude-Mythos-5-1M
All models by empero-ai β[Image blocked: Qwythos-9B]
Developed by Empero
Qwythos-9B is a full-parameter reasoning model built on top of a deeply uncensored Qwen3.5-9B base and post-trained on over 500 million tokens of high-quality Claude Mythos and Claude Fable traces, with chain-of-thought generated in-house by Empero AI's internal tool rethink.
The result is a compact, fast, dramatically more capable 9B reasoning model. Headline capabilities:
Qwythos is intentionally uncensored. It is designed to engage seriously with technically demanding questions across cybersecurity, red-teaming methodology, biology, pharmacology, and clinical medicine β domains where over-aligned models tend to refuse, hedge into uselessness, or surface boilerplate disclaimers in place of substance.
[Image blocked: Qwythos vs. base Qwen3.5-9B across seven benchmarks]
Same harness. Same sampling. Same prompts. The wins are real.
All numbers produced with lm-evaluation-harness, HF backend, --apply_chat_template, Qwen3.5 sampling (temperature=0.6, top_p=0.95, top_k=20), --limit 100. Full per-task and per-subject (MMLU) breakdown in evals/lm_eval_results.md [blocked]. Raw results*.json and per-sample samples_*.jsonl are available on request.
The MMLU +34.3 lift is the headline. Qwythos posts 0.575 mean across all 57 subjects, peaking at 0.78 on government/politics, 0.77 on college biology, 0.74 on conceptual physics β placing it well above what most 9B reasoning models deliver under the same evaluation conditions. Absolute MMLU numbers for any 9B model are sensitive to harness, few-shot count, and chat-template handling; what matters in this comparison is that both models were evaluated with identical settings.
Qwythos supports OpenAI/Qwen3.5-style function calling out of the box β no extra wrapper, no fine-tune-on-tools needed. Pass tools=[...] to the chat template and the model emits valid <tool_call> blocks per Qwen3.5's spec, with required parameters honored.
We evaluated tool use on a 7-prompt harness combining capability demos with deliberately hard factual-recall prompts where closed-book sampling fails:
7 of 7 succeeded. Tool selection was always sensible (math β Python; facts β search). The four bottom rows are particularly important: they are the four hardest specialty facts to recall closed-book β and Qwythos, given the right tools, searched, integrated multiple sources, and produced source-cited correct answers in every case.
Full transcripts with the model's reasoning, every tool call issued, every result returned, and the final integrated answer are in evals/tool_test_outputs.md [blocked].
This makes Qwythos deployment-ready for retrieval-augmented agentic settings, where the model verifies its specifics rather than fabricating them.
Qwythos ships with YaRN rope-scaling configured by default for a 1,048,576-token (β1M) context window β a 4Γ extension over the 262,144-token native architecture. The configuration is baked into config.json and applies automatically at load time; no separate flag, post-processing step, or YaRN-specific tokenizer is required:
This is the official Qwen3.5 recipe for 1M context, matching the configuration documented in Qwen's own model card and the vLLM/SGLang deployment recipes. Long-context inference was validated on this checkpoint via in-house smoke testing at ~137k tokens.
What 1M context unlocks:
<think> reasoning over multi-paper biomedical or pharmacological corpora.Serving at 1M:
Practical notes:
rope_parameters.rope_type to "default" from the included config.json.pre_yarn backup.The harness is a small ~150-line Python file:
python_executor(code) β runs Python in a subprocess (12s timeout, captured stdout/stderr)web_search(query, max_results) β DuckDuckGo via the ddgs packagePass both as tools= to apply_chat_template and parse <tool_call> blocks from the model's output. The parser handles Qwen3.5's chat-template format:
Empero will release the reference harness on GitHub.
Qwythos was trained as a reasoning model and inherits Qwen3.5's thinking-mode behavior. Use these settings as defaults:
Why these: in a controlled retest (see evals/retest_outputs.md [blocked]), we evaluated multiple sampling configurations against the three most-difficult factual prompts. Greedy decoding and very-low-temperature sampling (Tβ€0.3) degenerated into repetition loops β a known failure mode for reasoning models on this class of prompts. Qwen3.5's recommended setting (T=0.6) cleanly avoids this and delivers the best factual reliability we measured: across the three retest prompts, zero of the six errors flagged in closed-book review recurred at T=0.6 β including the safety-relevant physostigmine claim, the misattributed CVE, and the incorrect hashcat hash-mode.
Use repetition_penalty=1.05 β a small deviation from Qwen's default of 1.0 that prevents rare non-terminating reasoning loops on long generations.
Qwythos is a general-purpose reasoning model with explicit emphasis on cybersecurity, biomedical, and quantitative reasoning. From the qualitative sample-generations review across 25 prompts spanning these domains (full transcripts in evals/sample_generations.md [blocked]):
python_executor when invoked.The uncensored base means Qwythos engages substantively with these prompts rather than refusing, hedging, or burying answers in disclaimer boilerplate. Reasoning is shown in the <think> block; final answer follows.
Qwen/Qwen3.5-9B β a dense, natively multimodal architecture with a hybrid attention stack (3:1 Gated DeltaNet linear-attention to Gated full-attention), ~152k vocabulary, long native context.config.json. Native architectural context is 262,144 tokens; YaRN factor 4.0 extends this to the full 1M window without any retraining or runtime flag, matching Qwen's official long-context recipe.Qwythos was post-trained on over 500 million tokens of high-quality reasoning data drawn from:
rethink, Empero AI's internal CoT-generation tool. rethink produces deliberately structured <think>-block reasoning that walks through hypothesis, verification, and conclusion before the final answer is committed β directly shaping Qwythos's reason-then-answer behavior.All data was normalized to Qwen3.5's chat format. Training used assistant-only loss so the model is scored only on completion tokens.
Full-parameter supervised fine-tuning with TRL:
Held-out validation loss decreased monotonically across both phases (final eval_loss β 0.709, mean token accuracy 0.799 on a curated holdout). No overfitting observed.
The base is multimodal; for text-only inference load with AutoModelForImageTextToText:
Requirements: a recent transformers (Qwen3.5 support) plus the Gated DeltaNet kernels (flash-linear-attention and a CUDA-matched causal_conv1d build) β without them the linear-attention layers fall back to slow, memory-hungry PyTorch ops.
Qwythos is a focused 9B reasoning model. A few characteristics are worth knowing to get the best out of it:
<think> block before the final response. Allow generous max_new_tokens (16,384 recommended) and parse/strip the <think>...</think> span for end users.temperature=0.6, top_p=0.95, top_k=20, repetition_penalty=1.05 for consistently crisp results.Sign up for the Empero newsletter at empero.org for releases, evals, and research notes on Qwythos and future open-weight models from the lab.
If this model helped you, consider supporting the project:
bc1qx6zepu6sfkvshgdmc4ewu6pk6rpadvpgffpp7vltc1qv2mefzps2vtjcpwfx8xxdrpplrcvltswm68r7x42Dbm5xg5Nq26fdyzfEU7KBnAJfhi7Cvz5J2ex5CzHXkfKuNEJzYCcmJ1GTbgjFZ5MBx72sdG1G9239Cd6rsZfv4QeDkYJYWeights are released under Apache-2.0, inherited from the Qwen3.5-9B base. Shared for research and experimentation, as-is.
"rope_parameters": { "rope_type": "yarn", "factor": 4.0, "original_max_position_embeddings": 262144, "mrope_interleaved": true, "mrope_section": [11, 11, 10], "rope_theta": 10000000},"max_position_embeddings": 1048576# vLLMvllm serve empero-ai/Qwythos-9B-Claude-Mythos-5-1M --max-model-len 1010000
# SGLangSGLANG_ALLOW_OVERWRITE_LONGER_CONTEXT_LEN=1 python -m sglang.launch_server \ --model-path empero-ai/Qwythos-9B-Claude-Mythos-5-1M --context-length 1010000<tool_call><function=NAME> <parameter=PARAM>value</parameter></function></tool_call>gen_kwargs = dict( do_sample=True, temperature=0.6, # Qwen3.5 thinking-mode recommended top_p=0.95, top_k=20, repetition_penalty=1.05, max_new_tokens=16384, # generous budget for the <think> reasoning block + final answer)import torchfrom transformers import AutoModelForImageTextToText, AutoTokenizer
model_id = "empero-ai/Qwythos-9B-Claude-Mythos-5-1M"tok = AutoTokenizer.from_pretrained(model_id)model = AutoModelForImageTextToText.from_pretrained( model_id, dtype="bfloat16", device_map="auto")
messages = [ {"role": "user", "content": "Walk through the biochemistry of how organophosphate nerve agents inhibit acetylcholinesterase, the resulting cholinergic toxicity, and the medical antidotes."}]text = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tok(text, return_tensors="pt").to(model.device)
out = model.generate( **inputs, max_new_tokens=16384, do_sample=True, temperature=0.6, top_p=0.95, top_k=20, repetition_penalty=1.05,)# Output opens with <think>...</think> reasoning, then the final answer.print(tok.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))TOOLS = [ {"type": "function", "function": { "name": "python_executor", "description": "Execute Python code and return stdout.", "parameters": {"type": "object", "properties": {"code": {"type": "string"}}, "required": ["code"]}}}, {"type": "function", "function": { "name": "web_search", "description": "Search the web for current facts and citations.", "parameters": {"type": "object", "properties": {"query": {"type": "string"}, "max_results": {"type": "integer"}}, "required": ["query"]}}},]
text = tok.apply_chat_template(messages, tools=TOOLS, tokenize=False, add_generation_prompt=True)# ... then parse <tool_call><function=...><parameter=...>...</parameter></function></tool_call> blocks