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Scion-35B-A3B

SkyIsNotGreen

GitHub: Build-scripts and docs  |  Forensics study  |  Runtime fork  |  Discussions A full 35B-A3B MoE in one 11.3 GB GGUF for llama.cpp. Ternary expert banks plus small trained corrections, with no full-precision masters and no full-model QAT. > **2.61 bpw** | **11.34 GB** (6.3x smaller than the BF16 reference) | **best PPL of the 2-bit class** | **task retention at Q4-class level, at about half of Q4_K_M's size**

Text generation

Input Text→ Output Text

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Scion-FlashNext-176B-A6B

SkyIsNotGreen

A full Qwen3.8-Flash-Next in one 57.62 GB GGUF for llama.cpp — **176B parameters in the file**: the 125B language model (6B activated) plus the 51.2B-parameter n-gram table (the base's 4B MTP head is not included). Ternary expert banks plus small trained corrections, and the n-gram table (PLE) in the per-row **Q4X160** container (26.24 GB) — no full-precision masters, no full-model QAT. Two larger containers ship in the same repo: the Q4_0-table file at 60.17 GB and the F16-body file at 65.80 GB. > **2.60 bpw** | **57.62 GB** (3.2x smaller than the FP8 release) | **smallest build of this model published on the Hub that we can see, pruned or not** | **cap_eval 15/20, 0 harness errors** | **HellaSwag 400 82.00%, Winogrande 400 77.50%** The name comes from grafting. A scion is the shoot grafted onto a rootstock, and here the trained corrections are grafted onto a 1.75 bpw ternary expert body.

Text generation

Input Text→ Output Text

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Monthly input tokens 10 MMonthly output tokens 2 M1 M = 1 million tokens · USD
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