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AI モデル 84759

• mms-tts-vmw↗

  facebook

• Qwen-Image-2.1-Uncensored-GGUF↗

  feeday

• ind-latn-100mb-after-ppt-Dp-100mb-packed-bfdiso-ckpt500_seed10↗

  francesca9805

• Swift-Bonsai-2-27B-NInfer↗

  fyb1214

  Swift-Bonsai-2 is UkisAI's reasoning-efficient derivative of Prism ML's Ternary Bonsai 2 27B, packaged here for the NInfer engine as `PQ2_0_G128` and `PTQ1_0_G128` artifacts. The ternary codes were moved **byte-for-byte** out of the source GGUF into the NInfer container — nothing was dequantized and requantized, so there is no second quantization loss. Both files are in the root of this repository — download them from the **Files** tab. Each file is a complete container — text tower, vision tower, MTP head, proposal head, tokenizer, chat template and media-processor resources. No adapter file, patch, or extra flag is needed. **No DFlash2 adapter.**

• wav2vec2-xlsr-nepali↗

  gagan3012

  Fine-tuned facebook/wav2vec2-large-xlsr-53 on Nepali using the Common Voice, and OpenSLR ne. Prediction: ['पारानाको ब्राजिली राज्यमा रहेको राजधानी', 'देवराज जोशी त्रिभुवन विश्वविद्यालयबाट शिक्षाशास्त्रमा स्नातक हुनुहुन्छ'] Reference: ['पारानाको ब्राजिली राज्यमा रहेको राजधानी', 'देवराज जोशी त्रिभुवन विश्वविद्यालयबाट शिक्षाशास्त्रमा स्नातक हुनुहुन्छ']

• medgemma-4b-it-ONNX↗

  geeek

• flux-game↗

  gokaygokay

• bert_uncased_L-8_H-256_A-4↗

  Google

  This is the set of 24 BERT models referenced in Well-Read Students Learn Better: On the Importance of Pre-training Compact Models (English only, uncased, trained with WordPiece masking). We have shown that the standard BERT recipe (including model architecture and training objective) is effective on a wide range of model sizes, beyond BERT-Base and BERT-Large. The smaller BERT models are intended for environments with restricted computational resources. They can be fine-tuned in the same manner as the original BERT models. However, they are most effective in the context of knowledge distillation, where the fine-tuning labels are produced by a larger and more accurate teacher. Our goal is to enable research in institutions with fewer computational resources and encourage the community to seek directions of innovation alternative to increasing model capacity.

• paligemma2-28b-pt-448-jax↗

  Google

• StandardBERT-DR↗

  ielabgroup