Model Releases
We quantized the new Ornith 1.5 9B and 35B-A3B
ornith lab dropped new ornith 1.5 today, a 9B dense with vision and a 35B-A3B MoE, both MIT, trained on a loop that generates its own tasks. in addition there was giant 397b model, but we didn't quant
ornith lab dropped new ornith 1.5 today, a 9B dense with vision and a 35B-A3B MoE, both MIT, trained on a loop that generates its own tasks. in addition there was giant 397b model, but we didn't quantize it (but if you want to try - we will do it) we made our AD (Atomic Dynamic) quants for both, 9B (14 builds) and 35B-A3B (13 builds), and measured them against stock llama.cpp quants (on the same imatrix) their mean KLD and top-1 against our own BF16 conversion Ornith-1.5-9B file size mean KLD top-1 Q8_0 9.53 GB 0.0022 97.94% AD-Q8_0-Q6_K 8.55 GB 0.0035 97.46% Q5_K_M 6.47 GB 0.0299 92.80% AD-Q5_K-Q4_K 5.93 GB 0.0255 93.10% AD-Q4_K-IQ4_XS 5.61 GB 0.0344 91.93% AD-IQ3_S-IQ3_XXS 4.29 GB 0.1441 83.44% Ornith-1.5-35B-A3B file size mean KLD top-1 Q6_K 28.51 GB 0.0167 94.63% AD-Q6_K-Q5_K 26.25 GB 0.0158 94.85% Q5_K_M 24.73 GB 0.0269 93.31% AD-Q5_K-Q4_K 22.14 GB 0.0251 93.52% Q4_K_M 21.17 GB 0.0477 91.01% AD-Q4_K-IQ4_XS 20.13 GB 0.0315 92.71% Collections on HF with the imatrix, the per-tensor layouts and everything else: https://huggingface.co/collections/AtomicChat/ornith-15-9b https://huggingface.co/collections/AtomicChat/ornith-15-35b-a3b Our local ai open source app https://atomic.chat (I'm cofounder). Feel free to ask any questions and share your feedback! submitted by /u/Fun-Meaning-6474 [link] [comments]
Related
- Question about Quant versus Size.
- [[ornith-15-397b-deepswe-56-35b-a3b-9b|Ornith-1.5 (397B [DeepSWE 56], 35B-A3B, 9B)]]
- Ornith-1.5 9B might not be bad at all
Source: r/LocalLLaMA | 2026-08-19