Qwythos-9B-v2 is the new and improved Qwythos — all the deep chain-of-thought reasoning of the base Qwythos, with the looping behavior fixed. The looping/degeneration that showed up under greedy or low-temperature decoding is trained out (6.7% → 0%), the native MTP head is restored, and the identity prompt is cleaned up — while knowledge and reasoning are held at (or above) the base Qwythos level.
The fix uses FTPO (Final-Token Preference Optimization): the exact token that starts a repetition loop is identified and the model is gently trained to prefer coherent alternatives at that one position, leaving the rest of the distribution — and therefore its knowledge and reasoning — untouched.
For full training details, evaluation numbers, and sample generations, see the base model card.
What's new vs. the base Qwythos
🔁 Looping behavior eliminated — repetition under greedy / low-temp decoding dropped 6.7% → 0%. Greedy decoding is now safe — you no longer need repeat-penalty as a band-aid.
🧩 MTP head restored — the native multi-token-prediction module is back in the -MTP- files, so speculative-decoding (--spec-type draft-mtp) works.
🧠 Reasoning preserved — MMLU / GSM8K / GPQA / ARC held at or above the base Qwythos level (see the model card).
🪪 Cleaner identity — states who it is once, only when asked.
🔓 Still intentionally uncensored, still 1M-token context (YaRN), still multimodal-capable (Qwen3.5 vision tower).
Files
Normal text weights — trunk only (32 blocks)
File
Quant
Size
Notes
Qwythos-9B-v2-Q4_K_M.gguf
Q4_K_M
5.34 GiB / 5.74 GB
recommended default — smallest practical, good quality
Qwythos-9B-v2-Q5_K_M.gguf
Q5_K_M
6.08 GiB / 6.52 GB
balanced quality / size
Qwythos-9B-v2-Q6_K.gguf
Q6_K
6.95 GiB / 7.46 GB
high quality
Qwythos-9B-v2-Q8_0.gguf
Q8_0
8.87 GiB / 9.53 GB
near-lossless
Qwythos-9B-v2-BF16.gguf
BF16
16.69 GiB / 17.92 GB
full precision (conversion base)
If you don't know which to pick, Q4_K_M is the right starting point.
MTP-enabled text weights (33 blocks, nextn_predict_layers = 1)
These embed the restored Qwen3.5-compatible MTP head. Use them with llama.cpp builds that support MTP draft speculation (--spec-type draft-mtp). The MTP matrices are retained at Q8_0 in every quantized variant.
File
Quant
Size
Notes
Qwythos-9B-v2-MTP-Q4_K_M.gguf
Q4_K_M + MTP
5.50 GiB / 5.90 GB
recommended MTP default
Qwythos-9B-v2-MTP-Q5_K_M.gguf
Q5_K_M + MTP
6.25 GiB / 6.71 GB
balanced quality / size
Qwythos-9B-v2-MTP-Q6_K.gguf
Q6_K + MTP
7.14 GiB / 7.67 GB
high quality
Qwythos-9B-v2-MTP-Q8_0.gguf
Q8_0 + MTP
9.11 GiB / 9.79 GB
near-lossless
Qwythos-9B-v2-MTP-BF16.gguf
BF16 + MTP
17.14 GiB / 18.41 GB
full precision (conversion base)
Vision projector — for image input
File
Size
Notes
mmproj-Qwythos-9B-v2-BF16.gguf
0.86 GiB / 0.92 GB
CLIP-style vision encoder + projector at BF16 native precision; required for images, pairs with any text quant above
The vision tower is inherited unchanged from Qwen3.5-9B — it was frozen through both the base Qwythos SFT and the v2 FTPO fine-tune, so image behavior matches base Qwen3.5-9B. This mmproj is interchangeable with any Qwen3.5-9B mmproj-*.gguf.
Qwythos is a hybrid model — a 3:1 mix of Gated-DeltaNet linear-attention (SSM) blocks and full-attention blocks. The linear-attention state tensors are disproportionately sensitive to low-bit quantization, so the K-quants here keep them at higher precision than the surrounding weights:
Quant
ssm_alpha
ssm_beta
ssm_out
Q6_K
Q8_0
Q8_0
Q8_0
Q5_K_M
Q8_0
Q8_0
Q6_K
Q4_K_M
Q8_0
Q8_0
Q6_K
The remaining SSM state tensors (ssm_a, ssm_conv1d, ssm_dt, ssm_norm) are kept at F32 by the converter. This preserves the hybrid/SSM blocks for a small (~2–4%) increase in file size over a flat K-quant. Q8_0 and BF16 are uniform and need no overrides.
Quick start
llama.cpp
bash
1llama-cli \2 -m Qwythos-9B-v2-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
Because v2's looping is trained out, --repeat-penalty is now optional and greedy decoding (--temp 0) stays coherent.
Ollama
ollama run hf.co/empero-ai/Qwythos-9B-v2-GGUF:Q4_K_M
LM Studio / jan / KoboldCpp
Drop any .gguf into your runtime's model directory. Qwythos uses the standard Qwen3.5 chat template; modern GGUF runtimes load it automatically from the file.
Honest note: all Qwythos training (base SFT and v2 FTPO) was text-only — the vision tower was never fine-tuned, so image-grounded reasoning inherits base Qwen3.5-9B behavior and has not been independently evaluated for this release.
Sampling recommendations
Qwythos is a reasoning model — every response opens with a <think>...</think> block before the answer.
Parameter
Value
temperature
0.6
top_p
0.95
top_k
20
repeat_penalty
1.05 (optional in v2)
max_new_tokens
16384
Unlike the base Qwythos, v2 does not loop under greedy / low-temperature decoding — you can use --temp 0 for deterministic runs without repetition. The 0.6-temperature settings above still match Qwen3.5's official thinking-mode recommendations for best quality.
Long context (1M tokens)
The GGUFs ship with YaRN rope-scaling baked in for a 1,048,576-token context window (4× the 262,144 native). Set -c up to 1048576; lower it to reduce KV-cache memory for shorter prompts. A single H100/H200-class GPU comfortably handles 256k–512k; the full 1M typically needs multi-GPU or aggressive KV-cache offload.
Conversion & verification
Converted and quantized with llama.cpp (convert_hf_to_gguf.py, llama-quantize), architecture qwen35, GGUF v3.
