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AI 模型 84759

• Gemma-4-26B-A4B-it-ROCmFP4-GGUF↗

  kingjones777

• EXAONE-Deep-32B↗

  LGAI-EXAONE

• Ternary-Bonsai-1.7B↗

  litert-community

  Ternary (INT2) `.litertlm` bundles of `prism-ml/Ternary-Bonsai-1.7B-unpacked`, packaged for the LiteRT-LM runtime and Google AI Edge Gallery. > **Recommended: `bonsai-1.7b-int2pc-4k-gpu.litertlm`.** The plain, unmodified build with > no optimizations applied. Everything else in this repo is **experimental** — longer > context or lighter activations, but whether a given one loads depends on your app's > LiteRT / LiteRT-LM version and dependencies. Start with the recommended build; reach for > an experimental one only if you specifically need what it offers. `32768` is the model's full context — `max_position_embeddings` for Ternary-Bonsai-1.7B is 32768, so the long-context builds here sit at the ceiling rather than truncating it.

• most-embed-de↗

  malteos

• Kant-Test-0.1-Mistral-7B-Mistral-7B-Instruct-v0.2-slerp-GGUF↗

  MaziyarPanahi

• mergekit-model_stock-qykbest-GGUF↗

  MaziyarPanahi

• videomae-base-finetuned-kinetics↗

  MCG-NJU

  VideoMAE model pre-trained for 1600 epochs in a self-supervised way and fine-tuned in a supervised way on Kinetics-400. It was introduced in the paper VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training by Tong et al. and first released in this repository. Disclaimer: The team releasing VideoMAE did not write a model card for this model so this model card has been written by the Hugging Face team. VideoMAE is an extension of Masked Autoencoders (MAE) to video. The architecture of the model is very similar to that of a standard Vision Transformer (ViT), with a decoder on top for predicting pixel values for masked patches.

• AI-Guru-GGUF↗

  mradermacher

• bellman-mistral-7b-instruct-v0.3-i1-GGUF↗

  mradermacher

• Borea-Phi-3.5-mini-Instruct-Coding-GGUF↗

  mradermacher