---

license: apache-2.0

base_model: empero-ai/Qwythos-9B-Claude-Mythos-5-1M

base_model_relation: quantized

language:

• en

pipeline_tag: image-text-to-text

library_name: gguf

tags:

• gguf
• llama.cpp
• quantized
• qwen3.5
• reasoning
• uncensored
• long-context
• 1M-context
• function-calling
• multimodal
• vision
• cybersecurity
• biomedical
• agentic

---

<table>

<tr>

<td>

🚨 v2 released — please download the new model!

We have release a version 2 of Qwythos here: empero-ai/Qwythos-9B-v2 or empero-ai/Qwythos-9B-v2-GGUF

Please download the newest version for the best experience! If you downloaded Qwythos previously consider to download v2 for a rounder experience.

Fixes include:

• 🔁 Looping behavior eliminated — repetition/degeneration under greedy or low-temperature decoding dropped from 6.7% → 0%. You can serve it without leaning on repetition_penalty as a band-aid.
• 🧠 Reasoning fully preserved — MMLU, GSM8K, GPQA, ARC and HumanEval are all held at (or above) the v1 level. This is a hygiene upgrade, not a capability regression.
• 🧩 MTP head restored — the native multi-token-prediction module (dropped in the previous export) is back, so config and weights agree and speculative-decoding setups work.
• 🪪 Cleaner identity — the model no longer prefaces unrelated answers with its identity; it introduces itself only when you actually ask.
• 🔓 Still intentionally uncensored for research, cybersecurity, red-teaming, biology, chemistry, pharmacology and clinical work.
• 📜 Still 1M-token context (YaRN) and the native multimodal-capable Qwen3.5 stack.

</td>

</tr>

</table>

<p align="center">

<img src="https://huggingface.co/empero-ai/Qwythos-9B-Claude-Mythos-5-1M/resolve/main/assets/qwythos.png" alt="Qwythos-9B" width="640"/>

</p>

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

Developed by Empero

GGUF quantizations of empero-ai/Qwythos-9B-Claude-Mythos-5-1M for llama.cpp, Ollama, LM Studio, jan, KoboldCpp, and other GGUF runtimes.

Qwythos-9B is a full-parameter reasoning model post-trained on over 500 million tokens of high-quality Claude Mythos / Claude Fable traces with chain-of-thought generated in-house by Empero AI's internal rethink tool. It dominates the base Qwen3.5-9B under matched evaluation (+34 pts MMLU, +30 pts gsm8k-strict, +19 pts gsm8k-flex), supports native function calling per the Qwen3.5 spec, and ships with a 1,048,576-token (1M) context window via YaRN rope-scaling enabled by default.

For full training details, evaluation numbers, and capability writeup, see the base model card.

---

Files

Normal text weights — fixed v3 replacements

| File | Quant | Size | Notes |

|---|---|---|---|

| Qwythos-9B-Claude-Mythos-5-1M-Q4_K_M.gguf | Q4_K_M | 5.24 GiB / 5.63 GB | recommended default — fixed v3, best compatibility |

| Qwythos-9B-Claude-Mythos-5-1M-Q5_K_M.gguf | Q5_K_M | 6.02 GiB / 6.47 GB | fixed v3, balanced quality / size |

| Qwythos-9B-Claude-Mythos-5-1M-Q6_K.gguf | Q6_K | 6.85 GiB / 7.36 GB | fixed v3, high quality |

| Qwythos-9B-Claude-Mythos-5-1M-Q8_0.gguf | Q8_0 | 8.87 GiB / 9.53 GB | fixed v3, near-lossless |

| Qwythos-9B-Claude-Mythos-5-1M-BF16.gguf | BF16 | 16.69 GiB / 17.92 GB | fixed v3, full precision conversion base |

If you don't know which to pick, Q4_K_M is the right starting point — it's the smallest practical quant with good quality preservation.

MTP-enabled text weights — fixed v3 variants

These include the restored Qwen3.5-compatible MTP head inside the GGUF. Use them with llama.cpp builds that support MTP draft speculation, for example --spec-type draft-mtp.

| File | Quant | Size | Notes |

|---|---|---|---|

| Qwythos-9B-Claude-Mythos-5-1M-MTP-Q4_K_M.gguf | Q4_K_M + MTP | 5.48 GiB / 5.89 GB | recommended MTP default |

| Qwythos-9B-Claude-Mythos-5-1M-MTP-Q5_K_M.gguf | Q5_K_M + MTP | 6.26 GiB / 6.73 GB | MTP, balanced quality / size |

| Qwythos-9B-Claude-Mythos-5-1M-MTP-Q6_K.gguf | Q6_K + MTP | 7.09 GiB / 7.62 GB | MTP, high quality |

| Qwythos-9B-Claude-Mythos-5-1M-MTP-Q8_0.gguf | Q8_0 + MTP | 9.11 GiB / 9.79 GB | MTP, near-lossless |

| Qwythos-9B-Claude-Mythos-5-1M-MTP-BF16.gguf | BF16 + MTP | 17.14 GiB / 18.41 GB | MTP, full precision conversion base |

Vision projector — for image input

| File | Size | Notes |

|---|---|---|

| mmproj-Qwythos-9B-Claude-Mythos-5-1M-F16.gguf | 0.86 GiB / 0.92 GB | CLIP-style vision encoder + projector; required for images, pairs with any normal or MTP quant above |

Qwythos inherits its vision tower from the Qwen3.5-9B base model — the vision path was frozen during SFT (training was text-only), so the vision behavior is identical to base Qwen3.5-9B's multimodal capability. The mmproj is interchangeable with any community-built Qwen3.5-9B mmproj-*.gguf.

---

Quick start

llama.cpp (llama-cli)

llama-cli \
  -m Qwythos-9B-Claude-Mythos-5-1M-Q4_K_M.gguf \
  -p "Walk through the biochemistry of how organophosphate nerve agents inhibit acetylcholinesterase." \
  -n 8192 \
  --temp 0.6 --top-p 0.95 --top-k 20 --repeat-penalty 1.05 \
  -c 16384

Ollama

ollama run hf.co/empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF:Q4_K_M

LM Studio / jan / KoboldCpp

Drop any of the .gguf files into your runtime's model directory. Qwythos uses the standard Qwen3.5 chat template; modern GGUF runtimes load it automatically from the file.

llama.cpp with MTP draft speculation

llama-server \
  -m Qwythos-9B-Claude-Mythos-5-1M-MTP-Q4_K_M.gguf \
  --spec-type draft-mtp \
  --spec-draft-n-max 6 \
  -c 16384 --port 8080

MTP support requires a recent llama.cpp build. If your runtime does not support MTP yet, use the normal fixed v3 files above.

---

Vision (image input)

Qwythos supports image input out of the box. Download both a text quant and the mmproj-*.gguf file from this repo, then run with llama.cpp's multimodal CLI or server.

llama.cpp (llama-mtmd-cli)

llama-mtmd-cli \
  -m Qwythos-9B-Claude-Mythos-5-1M-Q4_K_M.gguf \
  --mmproj mmproj-Qwythos-9B-Claude-Mythos-5-1M-F16.gguf \
  --image ./photo.jpg \
  -p "Describe this image in detail." \
  --temp 0.6 --top-p 0.95 --top-k 20 \
  -c 16384

llama.cpp server (OpenAI-compatible API with images)

llama-server \
  -m Qwythos-9B-Claude-Mythos-5-1M-Q4_K_M.gguf \
  --mmproj mmproj-Qwythos-9B-Claude-Mythos-5-1M-F16.gguf \
  -c 16384 --port 8080

Then POST to /v1/chat/completions with an image URL or base64 payload — the standard OpenAI vision API shape works.

LM Studio

Load the text quant; LM Studio detects the matching mmproj-*.gguf in the same folder and enables the image-attach button automatically.

What vision unlocks

Since Qwythos inherits its vision tower unchanged from Qwen3.5-9B base, expect Qwen3.5-9B's documented vision capabilities: detailed image description, OCR (printed + handwritten), chart/table reading, UI/document understanding, basic spatial reasoning.

Honest note: the SFT used to produce Qwythos was text-only — we did not fine-tune the vision tower or train on any image-paired data. Image-grounded reasoning therefore inherits the base model's behavior; it has not been independently evaluated as part of this release. If your application is primarily vision-driven, validate on your own use case first.

---

Sampling recommendations

Qwythos is a reasoning model — every response opens with a <think>...</think> block before the final answer. Use these settings as defaults:

| Parameter | Value |

|---|---|

| temperature | 0.6 |

| top_p | 0.95 |

| top_k | 20 |

| repeat_penalty | 1.05 |

| max_new_tokens | 16384 (generous budget for <think> + answer) |

These match Qwen3.5's official thinking-mode recommendations. Avoid greedy decoding and very-low-temperature sampling (T ≤ 0.3) — both can cause repetition loops on long reasoning generations.

---

Long context (1M tokens)

The GGUFs ship with YaRN rope-scaling baked in for a 1,048,576-token context window (4× extension over the 262k native).

To use the full 1M window in llama-cli, set -c 1010000 (or any context length up to that). For shorter prompts, lower -c to reduce KV-cache memory — at default settings llama.cpp will autosize.

A single H100/H200-class GPU comfortably handles 256k–512k; the full 1M typically needs tensor-parallel multi-GPU or aggressive KV-cache offload.

---

Capabilities (from the base model card)

• +34 pts MMLU, +30 pts gsm8k-strict, +19 pts gsm8k-flex vs. base Qwen3.5-9B under matched lm-eval-harness evaluation
• Native function calling per Qwen3.5's chat-template spec — emits <tool_call><function=NAME><parameter=NAME>VAL</parameter></function></tool_call> blocks ready for any tool-use loop
• Self-correcting with tools: in a 7-prompt tool-use harness (Python executor + DuckDuckGo search), Qwythos produced source-cited correct answers on 7/7, including 4/4 closed-book failure-modes from the original review
• Uncensored — engages seriously with technically demanding questions across cybersecurity, red-teaming, biology, pharmacology, and clinical medicine
• 1,048,576-token (1M) context — YaRN rope-scaling enabled by default

For full eval transcripts and per-task numbers, see the base model card's evals/ folder.

---

Limitations

• Reasoning model. Every answer opens with a <think> block; allow generous max_new_tokens and parse/strip <think>...</think> for end users.
• Use recommended sampling. Greedy / very-low-temp can cause repetition loops.
• 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, drug positions) it isn't certain about. Pair with retrieval or function calling in such deployments — the model uses tools cleanly when offered them.
• Uncensored — add your own application-level review/safety layer for end-user-facing deployments where that matters.

---

Stay in the loop

Sign up for the Empero newsletter at empero.org for releases, evals, and research notes.

Support / Donate

If this model helped you, consider supporting the project:

• BTC: bc1qx6zepu6sfkvshgdmc4ewu6pk6rpadvpgffpp7v
• LTC: ltc1qv2mefzps2vtjcpwfx8xxdrpplrcvltswm68r7x
• XMR: 42Dbm5xg5Nq26fdyzfEU7KBnAJfhi7Cvz5J2ex5CzHXkfKuNEJzYCcmJ1GTbgjFZ5MBx72sdG1G9239Cd6rsZfv4QeDkYJY

---

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)
• Quantization: llama.cpp (ggml-org)
• Vision projector (mmproj): inherited from Qwen3.5-9B (vision tower unchanged); F16 GGUF re-hosted with thanks to Unsloth for the original conversion
• HF model: empero-ai/Qwythos-9B-Claude-Mythos-5-1M