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Qwythos-9B-Claude-Mythos-5-1M-GGUF
KikoCis/Qwythos-9B-Claude-Mythos-5-1M-GGUF
Qwythos-9B-Claude-Mythos-5-1M-GGUF is a machine learning model from KikoCis. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
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.gguf39.8 GB · 100%
At a glance
- License
- apache-2.0
- Access
- Public
- Created
- Jul 14, 2026
- Updated
- Jul 14, 2026
- SHA
- ce7f3f24
Base models
Model card
From the Hugging Face model README
Qwythos-9B-Claude-Mythos-5-1M — full GGUF ladder + KLD fidelity metrics
This is NOT my model. All weights and training are by empero-ai (Qwythos-9B-Claude-Mythos-5-1M, Apache-2.0). This is an independent GGUF repack whose value-add is the objective fidelity measurement (KLD/PPL/Top-1 vs the Q8 reference) + provenance that the existing community GGUFs don't publish. No weights were modified.
✅ Recommended files
| Use case | File | Size | Top-1 vs Q8 |
|---|---|---|---|
| Archival / best | Qwythos-9B-Q8_0.gguf | 9.8 GB | 100% |
| Near-lossless | Qwythos-9B-Q6_K.gguf | 7.6 GB | 97.4% |
| Balanced | Qwythos-9B-Q5_K_M.gguf | 6.6 GB | 95.7% |
| Best compact (imatrix) | Qwythos-9B-IQ4_XS.gguf | 5.3 GB | 94.1% |
| Smallest (lossy) | Qwythos-9B-Q3_K_M.gguf | 4.7 GB | 83.7% |
Tip: IQ4_XS dominates Q4_K_M — smaller (5.3 vs 5.8 GB) and more faithful (94.1% vs 92.3% Top-1), thanks to the imatrix.
📊 Metrics — fidelity vs the Q8_0 reference
Measured with llama-perplexity --kl-divergence (68 chunks, n_ctx 512). KLD is the gold-standard quant-fidelity metric; Top-1 match = how often the quant's argmax equals the reference's.
| Quant | Size | PPL vs Q8 | KLD mean | KLD p50 | Top-1 match |
|---|---|---|---|---|---|
| Q8_0 | 9.79 GB | ref | ref | ref | 100.0% |
| Q6_K | 7.56 GB | +-0.03% | 0.0056 | 0.0016 | 97.4% |
| Q5_K_M | 6.64 GB | +-0.01% | 0.0129 | 0.0043 | 95.7% |
| Q4_K_M | 5.78 GB | +0.79% | 0.0356 | 0.0146 | 92.3% |
| IQ4_XS | 5.33 GB | +1.05% | 0.0230 | 0.0097 | 94.1% |
| Q3_K_M | 4.74 GB | +17.80% | 0.1576 | 0.0725 | 83.7% |
Reference = Q8_0, not BF16. Qwythos is a hybrid
qwen35arch (SSM + attention, like Ornith), which llama.cpp does not lower cleanly from safetensors. So the ladder is requantized from empero-ai's official Q8_0 (near-lossless) with--allow-requantize+ an imatrix, and fidelity is measured against that Q8_0. (Q3_K_M shows real degradation — 83.7% / +17.8% PPL; use IQ4_XS+ for quality.)
🚀 Run it
ollama run hf.co/KikoCis/Qwythos-9B-Claude-Mythos-5-1M-GGUF:Q5_K_M
llama-server -m Qwythos-9B-Q5_K_M.gguf -c 32768
Sampling: qwen3.5-family reasoning models want temp 1.0 / top_p 0.95 — low temp causes repetition loops. For Ollama with a recent build, use RENDERER qwen3.5 + PARSER qwen3.5.
⚠️ Honest scope
This release is fidelity-gated (KLD-measured), which is the differentiator over the existing community GGUFs. It is not an independent capability benchmark — for agentic/reasoning scores of Qwythos itself, see empero-ai's model card (their "Mythos-5" claims are theirs, not independently re-verified here).
🔁 Provenance
Requantized from the official Q8_0 with llama-quantize --allow-requantize + imatrix (qwythos.imatrix, 248 KB calibration corpus). Scripts in scripts/, per-quant KLD logs in reports/, SHA-256 in reports/artifact-sha256sums.txt.
Credit
Model © empero-ai (Apache-2.0). This repack + fidelity metrics: KikoCis.
- License
- apache-2.0
- Languages
- en
- Created
- Jul 14, 2026
- Updated
- Jul 14, 2026