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AI Models 84760

• 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.

• naija-address-normalizer↗

  sobowalebukola

  Converts **informal, landmark-based Nigerian addresses** into **structured JSON**. In Nigeria (and much of Africa) people locate places by landmarks and relative directions rather than street numbers — *"opposite Chicken Republic, after Mobil, Lekki Phase 1"*. Standard geocoders (Google, Nominatim, Pelias) are built for structured addresses and stumble on these. This model handles the first step: turning the messy text into clean fields a geocoder can work with. > **Scope:** this model does **not** predict coordinates. It outputs structured > fields; pass them to a geocoder (Nominatim / Photon / Pelias) for lat-long.

• tiny-gpt2↗

  sshleifer

• stable-diffusion-3.5-large↗

  Stability AI

• Ordis-1.5B-V355-VarGH-GGUF↗

  sugiken

• ESMplusplus_small↗

  Synthyra

• LosslessMegaCoder-Llama2-7B-Mini-GGUF↗

  TheBloke

• Nous-Capybara-7B-GGUF↗

  TheBloke

• halo2botnet50ts_256.a1h_in1k↗

  timm

• vit_base_patch16_clip_224.openai_ft_in12k_in1k↗

  timm