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AI 모델 85004

• t5-efficient-small-el4↗

  Google

• t5-v1_1-large↗

  Google

  T5 Version 1.1 includes the following improvements compared to the original T5 model- GEGLU activation in feed-forward hidden layer, rather than ReLU - see here. **Note**: T5 Version 1.1 was only pre-trained on C4 excluding any supervised training. Therefore, this model has to be fine-tuned before it is useable on a downstream task. Pretraining Dataset: C4 Authors: *Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu*

• Holo-3.1-4B↗

  Hcompany

• tiny-random-GraniteMoeForCausalLM↗

  hf-tiny-v2

• MiniMax-H3-x-Z-Image-hybrid↗

  hoidhxd

  A custom hybrid version of **MiniMax-H3** combining the **FL2VA** and **REF2VA** branches of the model. This hybrid layout was constructed at the raw safetensor payload level. No tensor conversion was performed when constructing the BF16 hybrid. The B25-B49 tensors were copied byte-for-byte from the REF2VA ZS05 BF16 model, while the remaining tensors come from the FL2VA ZS05 BF16 model.

• Huihui-gpt-oss-20b-BF16-abliterated-v2↗

  huihui-ai

  This model is a fine-tuned version of huihui-ai/Huihui-gpt-oss-20b-BF16-abliterated. It has been trained using TRL. Please refer to Quantization-Aware Training (QAT) for fine-tuning and quantization(huihui-ai/Huihui-gpt-oss-20b-mxfp4-abliterated-v2). **Advantages**: All core metrics (Loss/Acc/Entropy) improve synchronously, with a small gap between Eval and Train (<0.01), indicating strong generalization ability. Fine-tuning shows effect in just 400 steps, with high efficiency.

• pocket-tts-gguf↗

  idle-intelligence

• Qwen3.6-1.3B-A1.3B-small↗

  inference-optimization

• DEIMv2_DINOv3_X_COCO↗

  Intellindust

• mistralai_Mistral-Small-3.1-24B-Instruct-2503-GGUF↗

  ipsarkar