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AI Models 84759

• fasttext-ckb-vectors↗

  facebook

• mms-tts-cjs↗

  facebook

• textless_sm_de_en↗

  facebook

• ner-english-fast↗

  flair

  This is the fast 4-class NER model for English that ships with Flair. Based on Flair embeddings and LSTM-CRF. So, the entities "*George Washington*" (labeled as a **person**) and "*Washington*" (labeled as a **location**) are found in the sentence "*George Washington went to Washington*".

• multiberts-seed_14↗

  Google

• paligemma-3b-ft-okvqa-224↗

  Google

• t5-efficient-mini-nl24↗

  Google

• Llama-3-8B-Instruct-262k↗

  gradientai

• Pantheon-Reasoning-26B-A4B-1.1-V2↗

  Gryphe

• Qwen3.8-Flash-Next-cmf↗

  infosave

  Qwen3.8-Flash-Next — a 125B hybrid mixture-of-experts model, 6B active per token (36 Gated DeltaNet + 12 sparse-attention layers, 512 experts per layer, 51B n-gram table, 262k context, thinking mode) — as CMF files, text only. It runs with `cortiq`, a Rust engine with no Python. Experts stream from the memory-mapped file as tokens need them, so the default 77 GB file runs on one GPU (measured from a 12 GB VRAM budget up to a whole 32 GB card) with the rest in system RAM. **RTX 3090 (24 GB), cortiq 0.8.15: 49.1 tok/s decode; first token after a 1,000-token prompt in 9.9 s.** RTX 5090 (cortiq 0.8.8): 81.7 tok/s, 108.6 with speculative decoding. Use cortiq 0.8.15 or later (prebuilt binaries). Without `--no-think` the model reasons first; raise `--max-tokens` (default 256). Each `cortiq run` loads the model again (10–30 s to the first token); for chat, keep it loaded with `cortiq serve`.