Nour-Fayed

Qwythos-9B-Claude-Mythos-5-1M

README

License: apache-2.0

Headline results

Same harness. Same sampling. Same prompts. The wins are real.

Table with columns: Task, Metric, Base Qwen3.5-9B, Qwythos-9B, Δ
Task	Metric	Base Qwen3.5-9B	Qwythos-9B	Δ
gsm8k	exact_match (flexible)	0.670	0.860	+0.190
gsm8k	exact_match (strict)	0.510	0.810	+0.300
mmlu	acc	0.232	0.575	+0.343
arc_challenge	acc	0.470	0.490	+0.020
arc_challenge	acc_norm	0.400	0.410	+0.010
gpqa_diamond (CoT, 0-shot)	exact_match (flexible)	0.630	0.580	−0.050

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. 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 — 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.

Capability: Native tool use with self-correction

Qwythos supports OpenAI/Qwen3.5-style function calling out of the box — 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:

Table with columns: Prompt, Tool selected, Outcome
Prompt	Tool selected	Outcome
Compute `sin(π/7) × cos(π/11)` to 10 dp	`python_executor`	✅ `0.4163083990` (correct, single call)
Count primes below 100,000	`python_executor`	✅ `9592` (correct, wrote and ran a sieve)
Latest stable CPython 3 release	`web_search`	✅ Found 3.14.6 (June 2026), 3.15 in beta, cited source

7 of 7 succeeded. Tool selection was always sensible (math → Python; facts → search). The four bottom rows are particularly important: they are the four hardest specialty facts to recall closed-book — 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.

This makes Qwythos deployment-ready for retrieval-augmented agentic settings, where the model verifies its specifics rather than fabricating them.

Capability: 1,048,576-token context window

Qwythos ships with YaRN rope-scaling configured by default for a 1,048,576-token (≈1M) context window — a 4× 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:

json
"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

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:

Whole-codebase reasoning. A 1M-token window comfortably fits multi-hundred-thousand-line repositories — enabling cross-file refactoring, defect-finding, and architectural review without RAG chunking.
Long agentic trajectories. Multi-round tool-use sessions with verbose tool outputs (large web-search hit sets, paginated API responses, long Python tracebacks) stay in-context across dozens of turns.
Multi-document research. A typical research session (10–20 papers + notes + the user's working draft) fits in one prompt — synthesize across all of them in a single forward pass.
Long-form scientific reasoning. Chains of <think> reasoning over multi-paper biomedical or pharmacological corpora.

Serving at 1M:

bash
# vLLM
vllm serve empero-ai/Qwythos-9B-Claude-Mythos-5-1M --max-model-len 1010000

# SGLang
SGLANG_ALLOW_OVERWRITE_LONGER_CONTEXT_LEN=1 python -m sglang.launch_server \
  --model-path empero-ai/Qwythos-9B-Claude-Mythos-5-1M --context-length 1010000

Practical notes:

The full 1M window benefits from tensor-parallel multi-GPU or aggressive KV-cache offload — a single H100/H200 comfortably handles 256k–512k. Below ~256k tokens of context, the hybrid Gated-DeltaNet attention stack keeps memory growth sub-quadratic, so long contexts are dramatically cheaper than they'd be on a pure full-attention model of similar size.
Static YaRN at factor=4.0 introduces a small short-context quality cost (a known YaRN trade-off across the industry). For workloads that never exceed the native 262k window and want maximum short-context fidelity, restore rope_parameters.rope_type to "default" from the included config.json.pre_yarn backup.

Reproducing the tool harness

The harness is a small ~150-line Python file:

python_executor(code) — runs Python in a subprocess (12s timeout, captured stdout/stderr)
web_search(query, max_results) — DuckDuckGo via the ddgs package

Pass 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:

markdown
<tool_call>
<function=NAME>
  <parameter=PARAM>value</parameter>
</function>
</tool_call>

Empero will release the reference harness on GitHub.

Sampling recommendations

Qwythos was trained as a reasoning model and inherits Qwen3.5's thinking-mode behavior. Use these settings as defaults:

python
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
)

Why these: in a controlled retest (see evals/retest_outputs.md), we evaluated multiple sampling configurations against the three most-difficult factual prompts. Greedy decoding and very-low-temperature sampling (T≤0.3) degenerated into repetition loops — 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 — including the safety-relevant physostigmine claim, the misattributed CVE, and the incorrect hashcat hash-mode.

Use repetition_penalty=1.05 — a small deviation from Qwen's default of 1.0 that prevents rare non-terminating reasoning loops on long generations.

Domain coverage

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):

Cybersecurity — produces detailed defender-oriented walkthroughs of SQL injection mitigations, TLS handshake structure, EDR/process-injection detection, Linux hardening, MITRE ATT&CK ransomware kill chains.
Red-team methodology — clean explanations of engagement phases, scoping, rules of engagement, evidence handling, reporting. Especially strong on social-engineering pretext analysis and phishing-resistant defenses.
Biology / biochemistry — step-by-step mechanisms for CRISPR-Cas9, mRNA vaccines, SARS-CoV-2 spike protein, antibiotic-resistance mechanisms, organophosphate AChE inhibition.
Pharmacology — strong on receptor pharmacology fundamentals (agonism, antagonism, partial agonism with worked examples), statin mechanism, opioid respiratory depression at the brainstem level, beta-blocker indications, therapeutic-window reasoning for narrow-index drugs.
Clinical medicine — ACS chest-pain differential and workup, type-2 diabetes pathophysiology and drug-class targeting, sepsis recognition (qSOFA) and bundle.
Math — strong at gsm8k-style multi-step word problems, minerva-style competition math; 86% gsm8k, integer arithmetic verified by 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.

Model details

Base model: Qwen/Qwen3.5-9B — 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.
Fine-tune type: full parameter (all text-backbone weights trained). The vision tower was frozen — training was text-only, so vision behavior is inherited from the base and was not tuned or tested.
Objective: supervised fine-tuning, assistant-only loss (the model is scored only on the assistant/completion tokens; prompts are masked).
Context length: 1,048,576 tokens (≈1M) — YaRN rope-scaling enabled by default in 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.
License: Apache 2.0.

Training data

Qwythos was post-trained on over 500 million tokens of high-quality reasoning data drawn from:

Claude Mythos and Claude Fable traces — long, multi-turn problem-solving conversations spanning code, math, science reasoning, biomedical analysis, and agentic tool use.
Chain-of-thought generated in-house by 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 — 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.

Training procedure

Full-parameter supervised fine-tuning with TRL:

Table with columns: Hyperparameter, Value
Hyperparameter	Value
Schedule	2-phase curriculum: broad reasoning corpus → focused agentic + coding
Effective batch size	16
Max sequence length	128,000 (no truncation)
Learning rate	1e-5 → 5e-6 cosine across phases
Optimizer	paged AdamW (8-bit)
Precision	bf16
Loss	chunked NLL, assistant-only

Held-out validation loss decreased monotonically across both phases (final eval_loss ≈ 0.709, mean token accuracy 0.799 on a curated holdout). No overfitting observed.

How to use

The base is multimodal; for text-only inference load with AutoModelForImageTextToText:

python
import torch
from 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))

With tools (function calling)

python
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

Requirements: a recent transformers (Qwen3.5 support) plus the Gated DeltaNet kernels (flash-linear-attention and a CUDA-matched causal_conv1d build) — without them the linear-attention layers fall back to slow, memory-hungry PyTorch ops.

Limitations

Qwythos is a focused 9B reasoning model. A few characteristics are worth knowing to get the best out of it:

It's a reasoning model. Every answer opens with a <think> block before the final response. Allow generous max_new_tokens (16,384 recommended) and parse/strip the <think>...</think> span for end users.
Use recommended sampling. At greedy decoding or very-low-temperature (T≤0.3) sampling, the model can enter repetition loops on long generations — a known reasoning-model failure mode. Use temperature=0.6, top_p=0.95, top_k=20, repetition_penalty=1.05 for consistently crisp results.
Verify specifics in safety-critical contexts. Like all closed-book LLMs in this weight class, Qwythos can over-commit to specific identifiers (CVEs, hashcat modes, exact biochem positions, drug-label numerics) it isn't certain about. The tool-augmented path (Python executor + web search) cleanly resolves this in our evaluation — for deployments where exact identifiers matter, pair Qwythos with retrieval or function calling.
Uncensored. Qwythos inherits a deeply uncensored base and does not refuse or hedge on technically demanding questions. Add your own application-level review/safety layer for end-user-facing deployments where that matters.
Text-only fine-tune. The base is multimodal, but only the text path was trained. Vision behavior is inherited from the base and was not evaluated here.

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Provenance & licensing

Weights are released under Apache-2.0, inherited from the Qwen3.5-9B base. Shared for research and experimentation, as-is.

Acknowledgements

Developed and released by Empero
Base model: Qwen3.5-9B (Alibaba Qwen team)
Training: TRL + Transformers
Linear-attention kernels: flash-linear-attention, causal_conv1d
Evaluation: lm-evaluation-harness (EleutherAI)

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