People have been asking for the 27B since we put out Qwythos-9B. It's finally here. Qwythos-27B-v1 is an open-weights bf16 reasoning model on a Qwen3.5-27B base, Apache-2.0. The short pitch: it's the 9B sized up, and we didn't strip anything out of the base to make it fit — the native MTP head, the full vision tower, and the 1M context window are all still live.

• Weights + model card: empero-ai/Qwythos-27B-v1
• GGUFs: empero-ai/Qwythos-27B-v1-GGUF

The practical local bit

The recommended local starting point is Q4_K_M: it is 16.95 GB and runs on a 24GB card. The GGUF release has 11 files: Q4_K_M, Q5_K_M, Q6_K, Q8_0, and BF16, each in trunk-only and MTP-enabled variants, plus mmproj-Qwythos-27B-F16.gguf for image input. SHA256SUMS are included. The MTP variants work with llama.cpp's --spec-type draft-mtp. One conversion detail worth calling out: in every K-quant, we hold the entire Gated-DeltaNet state path — ssm_alpha, ssm_beta, and ssm_out — at Q8_0 or better. Those tensors are disproportionately sensitive to low-bit quantization. A default conversion can leave them at Q4/Q5/Q6 and quality degrades; this costs roughly 2–4% in file size. It is intentional, not a default llama.cpp conversion.

What is retained

The release is built around one point: nothing was ablated to make 27B fit. It keeps all three of these intact and active:

• Native MTP (nextn_predict_layers=1) for self-speculative decoding.
• The full vision tower; mmproj ships with the GGUFs.
• A 1,048,576-token context window: YaRN 4x over the 262,144-token native window.

It is a dense Qwen3.5-27B with hybrid attention: Gated-DeltaNet linear attention and a full-attention layer every fourth layer. Native Qwen3.5 function calling is present too: tools=[...] works without a wrapper.

Starting settings

For agentic or tool use, start with temperature=0.6, top_p=0.95, top_k=20, repetition_penalty=1.05, and max_new_tokens=16384 or more. For open-ended or creative work, use temperature 1.0. It's a reasoning model — every answer opens with a <think> block, so budget tokens accordingly and strip the block for end users. Unlike the 9B, greedy long-form generation came back loop-check clean, so you're not forced off low temperatures. Qwythos is intentionally uncensored for technical and research work, including cybersecurity and biomedical questions. If it is going in front of end users, add your own review layer and application controls. Happy to answer questions on the conversion or the quant choices.

A few weeks back we dropped Qwythos-9B here, and the loudest piece of feedback on this sub and in the HF discussions was some flavor of "it loops." Folks running GGUF under llama.cpp, greedy or low-temp, long reasoning traces, and it'd get stuck repeating itself until it hit the token wall.

Our answer at the time was the standard one: run it at temp 0.6, bump repetition_penalty to 1.05, give it a real reasoning budget, don't KV-cache-quant this arch. All true, all still good advice but it's a splint, not a fix. And someone called that out directly: paraphrasing, "finetunes like this are just hype, mine looped too and it wasn't the harness." Fair hit. The honest response to "it loops" isn't "you're holding it wrong", it's to make it stop looping.

So we did. Qwythos-9B-v2 is out, and the one-liner is: the looping behavior is trained out of the weights.

What we actually did. The method's called FTPO (Final-Token Preference Optimization). Instead of throwing more data at it, we went hunting for the loops elicited them at low temp, found the exact token where a coherent continuation tips into a repeat, and built preference pairs right there: the loop token as the rejected choice, the model's own coherent alternatives as chosen. ~2,000 pairs, LoRA r256, one short run, early-stopped the moment the loops were gone so we didn't nuke the model's actual knowledge. Narrow and surgical on purpose.

The number: looping rate under greedy decoding went 6.7% → 0.0% on our internal set. You can run it deterministic in an agent loop now without babysitting the sampler. repetition_penalty is optional instead of load-bearing.

Two other things we fixed while in there:

• MTP head restored. v1's export dropped the native multi-token-prediction tensors even though the config still claimed them. They're back (pulled from the pinned Qwen3.5-9B base), so --spec-type draft-mtp works.
• Identity toned down. v1 liked to announce who it was before unrelated answers. v2 says it once, when you ask.

For the GGUF crowd specifically (since that's where most of the loop reports came from): full set with and without MTP, Q4_K_M -> BF16, plus a vision mmproj. We kept the Gated-DeltaNet tensors (ssm_alpha/ssm_beta/ssm_out) at higher precision than the surrounding quant, because the hybrid linear-attention blocks are where low-bit rounding bites this arch hardest. Same standing advice: stick to the recommended sampling and don't KV-cache-quant Qwen3.5-9B — it's sensitive, same as the base was.

Still Apache-2.0, still uncensored, still 1M context (YaRN). It's a drop-in over v1 swap the model id and delete your repetition_penalty workaround.

• Model: https://huggingface.co/empero-ai/Qwythos-9B-v2
• GGUF: https://huggingface.co/empero-ai/Qwythos-9B-v2-GGUF

If you were one of the people it looped on — I'd genuinely like to know whether v2 holds up on your prompts, especially greedy / long traces / low reasoning budgets. That's the exact case we were targeting, and self-hosted setups in the wild are where it'll really get stress-tested.

We have just released our Claude Mythos Fine Tune based on synthetic CoT generated from Fable-5 and Mythos-5 session logs.

You can find the model here: https://huggingface.co/empero-ai/Qwythos-9B-Claude-Mythos-5-1M

GGUFs are also available here:
https://huggingface.co/empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF

We also have some sample outputs here for you: https://huggingface.co/empero-ai/Qwythos-9B-Claude-Mythos-5-1M/blob/main/evals/sample_generations.md

We hope you can find some use in it! :)