empero-ai / Qwythos-9B-Claude-Mythos-5-1M-GGUF like 862

huggingface.co · 2026-06-29 · by Long Nguyen ·generic
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empero-ai / Qwythos-9B-Claude-Mythos-5-1M-GGUF like 862

Qwythos-9B

| 🚨 v3 released — please redownload the GGUFs Hotfix for the chat template. If you downloaded this repo before v3, please redownload your GGUF. Fixes in v3: embedded chat template updated for preserved reasoning and adaptive thinking; fixes looping during long generation traces; fixes agentic use in harnesses like OpenCode, Abacus, Hermes, and Claude Code; | | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |

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

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4-bit

MTP Q4\_K\_M 5.89 GB Q4\_K\_M 5.63 GB

5-bit

MTP Q5\_K\_M 6.73 GB Q5\_K\_M 6.47 GB

6-bit

MTP Q6\_K 7.62 GB Q6\_K 7.36 GB

8-bit

MTP Q8\_0 9.79 GB Q8\_0 9.53 GB

16-bit

BF16 17.9 GBMTP BF16 18.4 GB

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