Initial release

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README.md

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+---
+license: apache-2.0
+base_model: empero-ai/Qwythos-9B-Claude-Mythos-5-1M
+base_model_relation: quantized
+language:
+  - en
+pipeline_tag: text-generation
+library_name: gguf
+tags:
+  - gguf
+  - llama.cpp
+  - quantized
+  - qwen3.5
+  - reasoning
+  - uncensored
+  - long-context
+  - 1M-context
+  - function-calling
+  - cybersecurity
+  - biomedical
+  - agentic
+---
+
+<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](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 — pick one quant
+
+| File | Quant | Size | Notes |
+|---|---|---|---|
+| `Qwythos-9B-Claude-Mythos-5-1M-Q4_K_M.gguf` | Q4_K_M | ~5.3 GB | **recommended default** — fits ~6–8 GB VRAM, strong quality |
+| `Qwythos-9B-Claude-Mythos-5-1M-Q5_K_M.gguf` | Q5_K_M | ~6.1 GB | balanced quality / size |
+| `Qwythos-9B-Claude-Mythos-5-1M-Q6_K.gguf` | Q6_K | ~6.9 GB | high quality |
+| `Qwythos-9B-Claude-Mythos-5-1M-Q8_0.gguf` | Q8_0 | ~8.9 GB | near-lossless |
+| `Qwythos-9B-Claude-Mythos-5-1M-BF16.gguf` | BF16 | ~17 GB | 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.
+
+---
+
+## 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.
+
+---
+
+## 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](https://huggingface.co/empero-ai/Qwythos-9B-Claude-Mythos-5-1M/tree/main/evals).
+
+---
+
+## 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](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)
+- HF model: [empero-ai/Qwythos-9B-Claude-Mythos-5-1M](https://huggingface.co/empero-ai/Qwythos-9B-Claude-Mythos-5-1M)