kokoro-82m-GGUF
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View details ↗DISCOVER & COMPARE
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View details ↗m2m100-418m-GGUF
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View details ↗MADLAD-400 is a 3B-parameter T5-based multilingual machine translation model covering **450+ languages**, trained on 1 trillion tokens. It was developed by Google and achieves strong results across a wide range of languages, with special emphasis on low-resource and underrepresented languages. Distributed under **Apache 2.0 license**. MADLAD-400 supports over 450 languages using ` ` target language tags (ISO 639 codes). This includes all major world languages plus many low-resource languages. See the original model card for the full language list.
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View details ↗mega-asr-GGUF
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View details ↗mimo-asr-GGUF
cstrGGUF conversion of `XiaomiMiMo/MiMo-V2.5-ASR` for **CrispStrobe/CrispASR**. Pure C++ inference — no Python, no Transformers, runs on Apple Silicon (Metal), CPU, and CUDA. The runtime is functional end-to-end: greedy decode through the 36-layer Qwen2 LM, full prefill + step-decode KV-cached graphs, prompt construction matching the upstream `MimoAudio.asr_sft` reference exactly. JFK transcription test passes verbatim. The default `mimo-asr-q4_k.gguf` (re-uploaded May 2026, PLAN #60d) ships with per-LM-layer Q/K/V projections fused into a single `model.layers.{i}.attn.qkv.{weight,bias}` tensor pair, yielding ~1.7× faster per-step decode on M1 vs the prior unfused layout (3058 ms/step → 1806 ms/step on a contended-disk run; ~1.1-1.2× pure-compute on a quiet box). The CrispASR runtime auto-detects either layout: the F16 file above keeps working unchanged via the separate-Q/K/V fallback path. Re-upload of a fused F16 is queued behind disk-headroom availability.
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View details ↗mini-omni2-GGUF
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View details ↗miotts-0.6b-GGUF
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