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AI 모델 449
- detr-doc-table-detection↗
TahaDouaji
detr-doc-table-detection is a model trained to detect both **Bordered** and **Borderless** tables in documents, based on facebook/detr-resnet-50. - **Developed by:** Taha Douaji - **Shared by [Optional]:** Taha Douaji - **Model type:** Object Detection - **Language(s) (NLP):** More information needed - **License:** More information needed - **Parent Model:** facebook/detr-resnet-50 - **Resources for more information:** - Model Demo Space - Associated Paper The model should not be used to intentionally create hostile or alienating environments for people.
- potion-base-32M↗
minishlab
This Model2Vec model is pre-trained using Tokenlearn. It is a distilled version of the baai/bge-base-en-v1.5 Sentence Transformer. It uses static embeddings, allowing text embeddings to be computed orders of magnitude faster on both GPU and CPU. It is designed for applications where computational resources are limited or where real-time performance is critical. It uses a larger vocabulary size than the potion-base-8M model which can be beneficial for tasks that require a larger vocabulary. Model2vec creates a small, static model that outperforms other static embedding models by a large margin on all tasks on MTEB. This model is pre-trained using Tokenlearn. It's created using the following steps: - Distillation: first, a model is distilled from a sentence transformer model using Model2Vec. - Training data creation: the sentence transformer model is used to create training data by creating mean output embeddings on a large corpus. - Training: the distilled model is trained on the training data using Tokenlearn. - Post-training re-regularization: after training, the model is re-regularized by weighting the tokens based on their frequency, applying PCA, and finally applying SIF weighting. The results show that **potion-base-32M** is the most performant static embedding model, reaching 94.66% of the performance of all-MiniLM-L6-v2 with an average score of 52.83 while being orders of magnitude faster.
- illustrious-xl-v2.0-GGUF↗
off-grid-ai
GGUF conversions of Illustrious XL v2.0 for **on-device** image generation with stable-diffusion.cpp. Converted with sd.cpp's `-M convert` so the tensors are correctly named and load directly — the community GGUF quants of this model are mis-exported and fail `get sd version from file`. `Q4_K_S` in a file name: stable-diffusion.cpp wrote the file with `--type q4_K`. Its weight tensors are Q4_K and the other tensors stay F16/BF16/F32. `Q4_K_S` is the closest standard GGUF label, so Hugging Face can list the file. This file was named `illustrious-xl-v2.0-Q4_K.gguf` before 2026-10-21; the contents are unchanged, and links pinned to an earlier commit still work. **Original model:** https://huggingface.co/OnomaAIResearch/Illustrious-XL-v2.0 — created by its respective authors. **License:** `creativeml-openrail-m` (carried over from the original; its use restrictions apply). This is a format conversion (quantization) only; all credit for the model belongs to the original creators.
- AI-image-detector↗
umm-maybe
*__NOTE__: Unless you are trying to detect imagery generated using older models such as VQGAN+CLIP, please use the updated version of this detector instead.* This model is a proof-of-concept demonstration of using a ViT model to predict whether an artistic image was generated using AI. It was created in October 2022, and as such, the training data did not include any samples generated by Midjourney 5, SDXL, or DALLE-3. It still may be able to correctly identify samples from these more recent models due to being trained on outputs of their predecessors.
- Huihui-gemma-4-31B-it-qat-q4_0-unquantized-abliterated-GGUF↗
huihui-ai
library_name: transformers license: apache-2.0 license_link: https://ai.google.dev/gemma/docs/gemma_4_license pipeline_tag: any-to-any base_model: - google/gemma-4-31B-it-qat-q4_0-unquantized tags: - abliterated - uncensored --- This is an uncensored version of google/gemma-4-31B-it-qat-q4_0-unquantized created with abliteration (see remove-refusals-with-transformers to know more about it). This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens. Please use the latest version of ollama
- Huihui-gemma-4-12B-it-qat-q4_0-unquantized-abliterated-GGUF↗
huihui-ai
library_name: transformers license: apache-2.0 license_link: https://ai.google.dev/gemma/docs/gemma_4_license pipeline_tag: any-to-any base_model: - google/gemma-4-12B-it-qat-q4_0-unquantized tags: - abliterated - uncensored - GGUF - MTP --- This is an uncensored version of google/gemma-4-12B-it-qat-q4_0-unquantized created with abliteration (see remove-refusals-with-transformers to know more about it). This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens. **Note**: For this model, both the thinking mode and the non-thinking mode have been completely abliterated. Only layers 23-36 have been abliterated.
- granite-3.0-1b-a400m-base↗
ibm-granite
**Model Summary:** Granite-3.0-1B-A400M-Base is a decoder-only language model to support a variety of text-to-text generation tasks. It is trained from scratch following a two-stage training strategy. In the first stage, it is trained on 8 trillion tokens sourced from diverse domains. During the second stage, it is further trained on 2 trillion tokens using a carefully curated mix of high-quality data, aiming to enhance its performance on specific tasks. **Supported Languages:** English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. Users may finetune Granite 3.0 models for languages beyond these 12 languages. **Intended use:** Prominent use cases of LLMs in text-to-text generation include summarization, text classification, extraction, question-answering, and more. All Granite Base models are able to handle these tasks as they were trained on a large amount of data from various domains. Moreover, they can serve as baseline to create specialized models for specific application scenarios.
- multilingual-MiniLMv2-L6-mnli-xnli↗
MoritzLaurer
--- language: - multilingual - en - ar - bg - de - el - es - fr - hi - ru - sw - th - tr - ur - vi - zh license: mit tags: - zero-shot-classification - text-classification - nli - pytorch metrics: - accuracy datasets: - multi_nli - xnli pipeline_tag: zero-shot-classification widget: - text: "Angela Merkel ist eine Politikerin in Deutschland und Vorsitzende der CDU" candidate_labels: "politics, economy, entertainment, environment" --- --- This multilingual model can perform natural language inference (NLI) on 100+ languages and is therefore also suitable for multilingual zero-shot classification. The underlying multilingual-MiniLM-L6 model was created by Microsoft and was distilled from XLM-RoBERTa-large (see details in the original paper and newer information in this repo). The model was then fine-tuned on the XNLI dataset, which contains hypothesis-premise pairs from 15 languages, as well as the English MNLI dataset. The main advantage of distilled models is that they are smaller (faster inference, lower memory requirements) than their teachers (XLM-RoBERTa-large). The disadvantage is that they lose some of the performance of their larger teachers.
- hubert-large-ll60k↗
facebook
The large model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. **Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out this blog for more in-detail explanation of how to fine-tune the model. The model was pretrained on Libri-Light.
- gemma-3-12b-it-unsloth-bnb-4bit↗
unsloth
Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. Gemma 3 models are multimodal, handling text and image input and generating text output, with open weights for both pre-trained variants and instruction-tuned variants. Gemma 3 has a large, 128K context window, multilingual support in over 140 languages, and is available in more sizes than previous versions. Gemma 3 models are well-suited for a variety of text generation and image understanding tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as laptops, desktops or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone. These models were trained on a dataset of text data that includes a wide variety of sources. The 27B model was trained with 14 trillion tokens, the 12B model was trained with 12 trillion tokens, 4B model was trained with 4 trillion tokens and 1B with 2 trillion tokens. Here are the key components: The combination of these diverse data sources is crucial for training a powerful multimodal model that can handle a wide variety of different tasks and data formats.