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AI 모델 84760
- EXAONE-4.0-32B-GGUF↗
LGAI-EXAONE
- Qwen3.5-27B-uncensored-heretic-v1-GGUF↗
llmfan46
- decider-4b↗
Mapika
A language model that does not generate text. It reads a state and one or more typed questions, each with an explicit option list, and returns a probability distribution over the options for every question from one forward pass. It is called from software, not chatted with. From v3 the state may include images. **This repository holds v3 (2026-10-20).** v2.1 stays available under the Hub tag `v2.1`, with its own card; v2 and v1 under `v2` and `v1`. - **Weights:** the text model at the repository root (Qwen3.5-4B-Base architecture, bf16, 8.4 GB), loaded as before by `decider.infer.Decider`, `decider.serve` and the GGUF builds. `vision/` holds the image part: the vision tower of Qwen3.5-4B-Base (unchanged, 0.6 GB) and the config of the image-text model. The image path reuses the text model's weights. - **Code:** decider-ai 1.10.0 for images; text works with 1.4.0 and later.
- M3↗
MGAMH
- beit-base-patch16-224-pt22k↗
Microsoft
BEiT model pre-trained in a self-supervised fashion on ImageNet-22k - also called ImageNet-21k (14 million images, 21,841 classes) at resolution 224x224. It was introduced in the paper BEIT: BERT Pre-Training of Image Transformers by Hangbo Bao, Li Dong and Furu Wei and first released in this repository. Disclaimer: The team releasing BEiT did not write a model card for this model so this model card has been written by the Hugging Face team. The BEiT model is a Vision Transformer (ViT), which is a transformer encoder model (BERT-like). In contrast to the original ViT model, BEiT is pretrained on a large collection of images in a self-supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. The pre-training objective for the model is to predict visual tokens from the encoder of OpenAI's DALL-E's VQ-VAE, based on masked patches.
- 5GCECKJP9uFn1TxPdCvCDh1ACzQRxEkdCMrcgTMPRXhbTQhW_vgg↗
minatolegend0312
- c4ai-command-r-plus-gguf↗
mmnga
- ModernBERT-large-zeroshot-v2.0↗
MoritzLaurer
This model is answerdotai/ModernBERT-large fine-tuned on the same dataset mix as the `zeroshot-v2.0` models in the Zeroshot Classifiers Collection. The following hyperparameters were used during training: - learning_rate: 9e-06 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.06 - num_epochs: 2
- llava-v1.5-7b-llamafile↗
mozilla-ai
**Model type:** LLaVA is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on GPT-generated multimodal instruction-following data. It is an auto-regressive language model, based on the transformer architecture. Llama 2 is licensed under the LLAMA 2 Community License, Copyright (c) Meta Platforms, Inc. All Rights Reserved. **Where to send questions or comments about the model:** https://github.com/haotian-liu/LLaVA/issues
- Qwen-Image-2.1-PE-MTP-GGUF↗
mozophe
GGUF builds of the Qwen-Image 2.1 prompt enhancers (PE), with the Qwen3.5 multi-token prediction (MTP) head included, so llama.cpp can use speculative decoding (`--spec-type draft-mtp`). These files are made for the llama.cpp backend of ComfyUI-Qwen-Image-2.1-PromptEnhancer-MTP, which downloads them automatically. They also work with a plain `llama-server` (see Usage). The PE models don't ship an MTP head. It is grafted from the base model they were fine-tuned from, `Qwen/Qwen3.5-9B` (the same 15 `mtp.*` tensors the ComfyUI node uses). Community GGUFs of these models have no MTP head and no Q8_0.