--- 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 ---
## 🚨 v2 released — please download the new model! We have release a version 2 of Qwythos here: **[empero-ai/Qwythos-9B-v2](https://huggingface.co/empero-ai/Qwythos-9B-v2)** or **[empero-ai/Qwythos-9B-v2-GGUF](https://huggingface.co/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.

Qwythos-9B

# Qwythos-9B-Claude-Mythos-5-1M-GGUF **Developed by [Empero](https://empero.org)** GGUF quantizations of **[empero-ai/Qwythos-9B-Claude-Mythos-5-1M](https://huggingface.co/empero-ai/Qwythos-9B-Claude-Mythos-5-1M)** for [llama.cpp](https://github.com/ggml-org/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](https://huggingface.co/empero-ai/Qwythos-9B-Claude-Mythos-5-1M)**. --- ## 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`) ```bash 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 ```bash 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 ```bash 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`) ```bash 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) ```bash 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 `...` 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 `` + 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 `VAL` 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](https://huggingface.co/empero-ai/Qwythos-9B-Claude-Mythos-5-1M/tree/main/evals). --- ## Limitations - **Reasoning model.** Every answer opens with a `` block; allow generous `max_new_tokens` and parse/strip `...` 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](https://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](https://empero.org) - Base model: [Qwen3.5-9B](https://huggingface.co/Qwen/Qwen3.5-9B) (Alibaba Qwen team) - Quantization: [llama.cpp](https://github.com/ggml-org/llama.cpp) (ggml-org) - Vision projector (`mmproj`): inherited from Qwen3.5-9B (vision tower unchanged); F16 GGUF re-hosted with thanks to [Unsloth](https://huggingface.co/unsloth) for the original conversion - HF model: [empero-ai/Qwythos-9B-Claude-Mythos-5-1M](https://huggingface.co/empero-ai/Qwythos-9B-Claude-Mythos-5-1M)