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AI 모델 84760

• detoxify-onnx↗

  gravitee-io

• TurboVLA↗

  H-EmbodVis

• opus-mt-en-az↗

  Helsinki-NLP

  * source group: English * target group: Azerbaijani * OPUS readme: eng-aze * model: transformer-align * source language(s): eng * target language(s): aze_Latn * model: transformer-align * pre-processing: normalization + SentencePiece (spm12k,spm12k) * download original weights: opus-2020-06-16.zip * test set translations: opus-2020-06-16.test.txt * test set scores: opus-2020-06-16.eval.txt - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-aze/README.md

• opus-mt-ur-en↗

  Helsinki-NLP

• bert-fa-base-uncased↗

  HooshvareLab

  We reconstructed the vocabulary and fine-tuned the ParsBERT v1.1 on the new Persian corpora in order to provide some functionalities for using ParsBERT in other scopes! Please follow the ParsBERT repo for the latest information about previous and current models. ParsBERT is a monolingual language model based on Google’s BERT architecture. This model is pre-trained on large Persian corpora with various writing styles from numerous subjects (e.g., scientific, novels, news) with more than `3.9M` documents, `73M` sentences, and `1.3B` words. You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the model hub to look for fine-tuned versions on a task that interests you.

• Wan-Alpha-v2.0↗

  htdong

• smolvla_libero↗

  HuggingFaceVLA

  This policy has been trained and pushed to the Hub using LeRobot. See the full documentation at LeRobot Docs. For a complete walkthrough, see the training guide. Below is the short version on how to train and run inference/eval: Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.

• Hy-Nomas↗

  腾讯

  **Hy-Nomas** is a video world generation model that supports free-form, multi-turn interaction through text instructions and camera control. It generates a world one video segment at a time, allowing users to introduce objects, change styles and scenes, describe actions, or control the camera as generation continues. The model combines a segment-wise autoregressive diffusion transformer (DiT), a Qwen text encoder, camera-conditioning modules, and a PixelUMM video decoder. A persistent key/value (KV) cache carries context between segments. The distilled inference configuration uses **8 denoising steps**, with **720 × 1280** output at **24 FPS**. A separate configuration supports video generation on a **single NVIDIA GeForce RTX 4090** using INT8 DiT weights, an INT8 KV cache, an FP8 decoder, and CPU offload. - **Free-form interaction:** Guide successive segments with instructions about subjects, actions, appearance, style, and environment. - **Camera control:** Supply camera trajectories alongside text to control viewpoint and movement. - **Continuous generation:** Reuse earlier context through a KV cache when extending the same world. - **Distilled inference:** Generate each segment with 8 DiT denoising steps and decode its latents with a fine-tuned PixelUMM decoder. - **Flexible deployment:** Use the standard sequence-parallel configuration or the single-GPU configuration, and interact through the web demo.

• granite-3.1-1b-a400m-instruct↗

  ibm-granite

  **Model Summary:** Granite-3.1-1B-A400M-Instruct is a 1B parameter long-context instruct model finetuned from Granite-3.1-1B-A400M-Base using a combination of open source instruction datasets with permissive license and internally collected synthetic datasets tailored for solving long context problems. This model is developed using a diverse set of techniques with a structured chat format, including supervised finetuning, model alignment using reinforcement learning, and model merging. **Supported Languages:** English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. Users may finetune Granite 3.1 models for languages beyond these 12 languages. **Intended Use:** The model is designed to respond to general instructions and can be used to build AI assistants for multiple domains, including business applications.

• XLM-Roberta-Large-Vit-B-32↗

  immich-app