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AI モデル 84757
- Llama-Calix-CLM-1.0↗
calix-ai-labs
- A-to-Zovya-RPG-v1-5↗
danbrown
- DeepSeek V4 Flash 0420 (Non-reasoning)↗
DeepSeek
- Ingot-IV-27B-Q2↗
dendriteholdings
A public mirror of a model that won on Ingot, a Bittensor subnet where miners quantize a fixed teacher model and the validator crowns the copy that stays closest to it. It is Prism ML's Ternary Bonsai 2 27B, refined: the same ternary codes, with the per-block scales and the norm weights re-tuned by the miner to bring it closer to Qwen3.8-27B. The weights are the miner's upload, byte for byte (`Ingot-IV-27B-PQ2_0.gguf`); `Ingot-IV-27B-PTQ1_0.gguf` repacks them losslessly at 1.75 bits per weight. Credit belongs to the miner, and to Prism ML for Bonsai 2.
- Falcon-3-1B↗
EasierAI
- genesis-4B-DEMO-GGUF↗
epsill0n
- EUPE-ViT-B↗
facebook
Running AI models on smart edge devices can unlock various user experiences, but presents challenges due to limited compute and the need to handle multiple tasks simultaneously. This requires a vision encoder with small size but powerful and versatile representations. We present our method, Efficient Universal Perception Encoder (EUPE), which offers both inference efficiency and universally good representations for diverse downstream tasks. We achieve this by distilling from multiple domain-expert foundation vision encoders. Unlike previous agglomerative methods that directly scale down from multiple teachers to an efficient encoder, we demonstrate the importance of first scaling up to a large proxy teacher and then distilling from this single teacher. Experiments show that EUPE achieves on-par or better performance than individual domain experts of the same size on diverse task domains and also outperforms previous agglomerative encoders. These are Vision Transformer and ConvNeXt models trained following the method described in the EUPE paper. 6 models are provided: - 3 ViT models including ViT-B16, ViT-S16, ViT-T16 - 3 ConvNeXt models including ConvNeXt-{T/S/B}
- mms-tts-mrw↗
facebook
- sapiens2-normal-0.8b↗
facebook
- OTel-LLM-1B-IT↗
farbodtavakkoli