The v2 GGUFs replace the original normal filenames and add explicit -MTP- variants. If you downloaded this repo before v2, please redownload your GGUF.
Fixes in v2:
tokenizer metadata normalized for Qwen3.5 GGUF runtimes;
embedded chat template updated for reliable tool/function calling and OpenCode-style agent loops;
Qwythos/Empero identity prompt embedded in the template;
MTP-enabled variants added as Qwythos-9B-Claude-Mythos-5-1M-MTP-*.gguf;
Q4/Q8 tool-calling, MTP draft speculation, 1M-context allocation, and vision projector smoke-tested with current llama.cpp.
Use the normal files for maximum runtime compatibility. Use the -MTP- files when you want llama.cpp MTP draft speculation.
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 v2 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 v2, best compatibility
Qwythos-9B-Claude-Mythos-5-1M-Q5_K_M.gguf
Q5_K_M
6.02 GiB / 6.47 GB
fixed v2, balanced quality / size
Qwythos-9B-Claude-Mythos-5-1M-Q6_K.gguf
Q6_K
6.85 GiB / 7.36 GB
fixed v2, high quality
Qwythos-9B-Claude-Mythos-5-1M-Q8_0.gguf
Q8_0
8.87 GiB / 9.53 GB
fixed v2, near-lossless
Qwythos-9B-Claude-Mythos-5-1M-BF16.gguf
BF16
16.69 GiB / 17.92 GB
fixed v2, 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 — v2 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
1llama-cli \2 -m Qwythos-9B-Claude-Mythos-5-1M-Q4_K_M.gguf \3 -p "Walk through the biochemistry of how organophosphate nerve agents inhibit acetylcholinesterase."\4 -n 8192\5 --temp 0.6 --top-p 0.95 --top-k 20 --repeat-penalty 1.05\6 -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.
MTP support requires a recent llama.cpp build. If your runtime does not support MTP yet, use the normal v2 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.
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
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.
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