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View details ↗GLM-4.7-Flash-Coder-Q4_K_M-GGUF
ali-sys-1370基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗i2vgen-xl
ali-vilab基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗In-Context-LoRA
ali-vilab基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗text-to-video-ms-1.7b
ali-vilabThis model is based on a multi-stage text-to-video generation diffusion model, which inputs a description text and returns a video that matches the text description. Only English input is supported. If you're looking for an exciting challenge and the opportunity to work with cutting-edge technologies in AIGC and large-scale pretraining, then we are the place for you. We are looking for talented, motivated and creative individuals to join our team. If you are interested, please send your CV to us. The text-to-video generation diffusion model consists of three sub-networks: text feature extraction model, text feature-to-video latent space diffusion model, and video latent space to video visual space model. The overall model parameters are about 1.7 billion. Currently, it only supports English input. The diffusion model adopts a UNet3D structure, and implements video generation through the iterative denoising process from the pure Gaussian noise video.
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View details ↗VACE-Wan2.1-1.3B-Preview
ali-vilab基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Qalam_Net_V2
Ali0044**Qalam-Net V2** (قلم-نت) is a high-performance Arabic Optical Character Recognition (OCR) system. Built on the **TrOCR** (Transformer-based OCR) architecture, it achieves superior accuracy by treats OCR as a sequence-to-sequence problem, mapping visual features directly to text tokens. The model utilizes a **Vision-Encoder-Decoder** framework, specifically optimized for the complexities of Arabic script (ligatures, cursive nature, and right-to-left orientation). - **End-to-End Transformer**: No reliance on traditional CNN-RNN architectures or complex preprocessing (like line segmentation). - **Arabic Script Specialist**: Fine-tuned on the `mssqpi/Arabic-OCR-Dataset` for robust handling of various Arabic fonts and styles. - **State-of-the-Art Accuracy**: Leverages pre-trained vision and language weights from `microsoft/trocr-base-handwritten`. - **Flexible Deployment**: Supports CUDA, MPS (Apple Silicon), and CPU execution.
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View details ↗M1
Ali32311基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗M2
Ali32311基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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ali878374基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗brain-tumor-vit-best
AliAFarThis is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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View details ↗vit-brain-tumour-mri
AliAljawi基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗alias3-micro-1026
aliasrobotics基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗YuFeng-XGuard-Reason-0.6B
Alibaba-AAIG基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗YuFeng-XGuard-Reason-8B
Alibaba-AAIG基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗RynnBrain-2B
Alibaba-DAMO-Academy基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗RynnBrain1.1-2B
Alibaba-DAMO-Academy💫 Project Page | 🤗 Hugging Face | 🤖 ModelScope | 📚 Cookbooks | 📄 arXiv * **[2026.07.16]** ✨✨ Release **RynnBrain 1.1** checkpoints (**2B / 9B / 122B-A10B**) on Hugging Face & ModelScope ! * **[2026.07.16]** ✨✨ Release the **RynnBrain 1.1** Technical Report on arXiv ! We present **RynnBrain 1.1**, a systematic upgrade of RynnBrain for embodied intelligence. RynnBrain 1.1 is released in three scales: **2B**, **9B**, and **122B-A10B**, extending the model family from compact dense models to its first 122B-level sparse-MoE model.
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View details ↗RynnBrain1.1-9B
Alibaba-DAMO-Academy基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗RynnWorld-Teleop
Alibaba-DAMO-Academy基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗mgp-str-base
alibaba-damoMGP-STR base-sized model is trained on MJSynth and SynthText. It was introduced in the paper Multi-Granularity Prediction for Scene Text Recognition and first released in this repository. MGP-STR is pure vision STR model, consisting of ViT and specially designed A^3 modules. The ViT module was initialized from the weights of DeiT-base, except the patch embedding model, due to the inconsistent input size. Images (32x128) are presented to the model as a sequence of fixed-size patches (resolution 4x4), which are linearly embedded. One also adds absolute position embeddings before feeding the sequence to the layers of the ViT module. Next, A^3 module selects a meaningful combination from the tokens of ViT output and integrates them into one output token corresponding to a specific character. Moreover, subword classification heads based on BPE A^3 module and WordPiece A^3 module are devised for subword predictions, so that the language information can be implicitly modeled. Finally, these multi-granularity predictions (character, subword and even word) are merged via a simple and effective fusion strategy.
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View details ↗gte-multilingual-base
alibaba-nlp-community基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗core-emb-2b
Alibaba-NLP基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗core-emb-8b
Alibaba-NLP基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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