Microsoft huggingface.

• Microsoft huggingface Pretraining large neural language models, such as BERT, has led to impressive gains on many natural language processing (NLP) tasks. Spaces using microsoft/git-base-coco 53. 2, Q&A content from StackOverflow, competition code from code_contests, and synthetic Python textbooks and exercises generated by gpt-3. Microsoft believes Responsible AI is a shared responsibility and we have identified six principles and practices to help organizations address risks, innovate, and May 9, 2025 · Phi-4 Available on HuggingFace: A Big Thanks to How to Fine-Tune Phi-4 Locally? Phi 3 – Small Yet Powerful Models from Mi Microsoft Phi-4 Multimodal: Hands-on Guide. Building generative AI applications starts with model selection and picking the right model to suit your application needs. It offers a suite of optimized kernels, that support fast and lossless inference of 1. You may have additional consumer rights or statutory guarantees under your local laws which this agreement cannot change. Moreover, the model outperforms bigger models in reasoning capability and only behind GPT-4o-mini. 5 Microsoft Document AI | GitHub. 6K open-source models from the Hugging Face communi Jan 8, 2025 · We’re on a journey to advance and democratize artificial intelligence through open source and open science. 0 ML and above. In May, we announced a deepened partnership with Hugging Face and we continue to add more leading-edge Hugging Face models to the Azure AI model catalog on a monthly basis. When using the model, make sure that your speech input is also sampled at 16kHz. Apr 11, 2024 · [2024/04/12] 🔥🔥🔥 Rho-Math-v0. Text Generation • Updated Feb 24 • 548k • • 2. Contribute to huggingface/blog development by creating an account on GitHub. The Azure AI Model Catalog offers over 1. Feb 13, 2025 · 📢 [GitHub Repo] [OmniParser V2 Blog Post] Model Summary OmniParser is a general screen parsing tool, which interprets/converts UI screenshot to structured format, to improve existing LLM based UI agent. Model description Kosmos-2. Connectors. 1 models released at 🤗 HuggingFace! Rho-Math-1B and Rho-Math-7B achieve 15. How to Get Started with the Model To get started with the model, you first need to make sure that transformers and torch are installed, as well as installing the following dependencies: Developer: Microsoft: Architecture: GRIN MoE has 16x3. 78K models, including foundation models from core partners and nearly 1. 58-2B-4T: Contains the packed 1. 🚀 Model paper. Feb 12, 2025 · Graphic User interface (GUI) automation requires agents with the ability to understand and interact with user screens. I understand that you are having trouble accessing Hugging Face ML endpoints on Azure Marketplace. When a cluster is terminated, the cache data is lost too. 6B active parameters when using 2 experts. Model Summary This repo provides the GGUF format for the Phi-3-Mini-4K-Instruct. May 24, 2023 · To address these challenges and enhance customers experience, we collaborated with Microsoft to offer a fully integrated experience for Hugging Face users within Azure Machine Learning Studio. Model tree for microsoft/CodeGPT-small-py. Model tree for microsoft/swinv2-tiny-patch4-window8-256. Deploy machine learning models and tens of thousands of pretrained Hugging Face transformers to a dedicated endpoint with Microsoft Azure. It is not meant to be used for clinical practice. Citation If you find MiniLM useful in your research, please cite the following paper: @misc{wang2020minilm, title={MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers}, author={Wenhui Wang and Furu Wei and Li Dong and Hangbo Bao and Nan Yang and Ming Zhou}, year={2020}, eprint={2002. 6% and 31. 5, augmented with a new data source that consists of various NLP synthetic texts and filtered websites (for safety and educational value). Dec 11, 2024 · HuggingFace はコミュニティ レジストリであり、Microsoft サポートの対象外です。 デプロイ ログを調べて、問題が Azure Machine Learning プラットフォームに関連するものか、HuggingFace トランスフォーマーに固有かを確認します。 We’re on a journey to advance and democratize artificial intelligence through open source and open science. 2 as LLM for a better commercial license . It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data. Microsoft Research: Description: phi-4 is a state-of-the-art open model built upon a blend of synthetic datasets, data from filtered public domain websites, and acquired academic books and Q&A datasets. HuggingFace; The demonstration uses a simple Windows Forms application with Semantic Kernel and Hugging Face connector to get the description of the images in a local folder provided by the user. Quantizations. , BitNet b1. 5 SLMs a Game-Change MAI-DS-R1 is a DeepSeek-R1 reasoning model that has been post-trained by the Microsoft AI team to improve its responsiveness on blocked topics and its risk profile, while maintaining its reasoning capabilities and competitive performance. Considering large language models (LLMs) have exhibited exceptional ability in language understanding, generation, interaction, and reasoning, we Interact with a chatbot that can process and generate text, images, audio, and video based on your input. Users have to apply it on top of the original LLaMA weights to get actual LLaVA weights. Aug 8, 2024 · Updated: Check out the Oct 2024 Recap Post Here · Learn why the Future of AI is: Model Choice . GIT (GenerativeImage2Text), base-sized GIT (short for GenerativeImage2Text) model, base-sized version. This model was added by Hugging Face staff. 58-bit weights optimized for efficient inference. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead. The Phi-3-Mini-4K-Instruct is a 3. Steps to use the Demo. While there are abundant AI models available for different domains and modalities, they cannot handle complicated AI tasks. The BitLinear layers quantize the weights using ternary precision (with values of -1, 0, and 1) and quantize the activations to 8-bit precision. It was introduced in the paper Expanding Language-Image Pretrained Models for General Video Recognition by Ni et al. Document Image Transformer (large-sized model) Document Image Transformer (DiT) model pre-trained on IIT-CDIP (Lewis et al. Swin Transformer (base-sized model) Swin Transformer model trained on ImageNet-1k at resolution 224x224. Intended Uses Primary Use Cases The model is intended for broad multilingual commercial and research use. However, using general purpose LLM models to serve as GUI agents faces several challenges: 1) reliably identifying interactable icons within the user interface, and 2) understanding the semantics of various elements in a screenshot and accurately associating the intended Model Summary The language model Phi-1 is a Transformer with 1. Adapters. Model tree for microsoft/deberta-v3-large. Collection including microsoft/git-large-textcaps. Model Card for UniXcoder-base Model Details Model Description UniXcoder is a unified cross-modal pre-trained model that leverages multimodal data (i. Model description LayoutLMv3 is a pre-trained multimodal Transformer for Document AI with unified text and image masking. Scalable and Versatile 3D Generation from images. GIT (GenerativeImage2Text), large-sized GIT (short for GenerativeImage2Text) model, large-sized version. A simple screen parsing tool towards pure vision based GUI agent - microsoft/OmniParser Pretraining large neural language models, such as BERT, has led to impressive gains on many natural language processing (NLP) tasks. Rho-Math-1B-Interpreter is the first 1B LLM that achieves over 40% accuracy on MATH. The model provides uses for general purpose AI systems and applications which require: Usage To transcribe audio samples, the model has to be used alongside a WhisperProcessor. It was introduced in the paper PubTables-1M: Towards Comprehensive Table Extraction From Unstructured Documents by Smock et al. 10957}, archivePrefix={arXiv}, primaryClass={cs. 5, using mistralai/Mistral-7B-Instruct-v0. Daniel Zügner (dzuegner@microsoft. This model does not have enough activity to be deployed to Inference API (serverless) yet. Microsoft Document AI | GitHub. This model was introduced in SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing by Junyi Ao, Rui Wang, Long Zhou, Chengyi Wang, Shuo Ren, Yu Wu, Shujie Liu, Tom Ko, Qing Li, Yu Zhang, Zhihua Wei, Yao Qian, Jinyu Li, Furu Wei. It aims to provide a robust foundation for language models to excel in mathematical problem-solving. Updated 10 days ago • 6 microsoft/DialoGPT-medium. Note: This model does not have a tokenizer as it was pretrained on audio alone. 5 is a multimodal literate model for machine reading of text-intensive images. We’re on a journey to advance and democratize artificial intelligence through open source and open science. 🟦 New open-source Image-to-3D model from Microsoft TRELLIS: Structured 3D Latents for Scalable and Versatile 3D Generation it's really good! the topology isn't clean, but it's a very very good 3D reference Microsoft gives no express warranties, guarantees or conditions. . To the extent permitted under your local laws, Microsoft excludes the implied warranties of merchantability, fitness for a particular purpose and non We’re on a journey to advance and democratize artificial intelligence through open source and open science. js and ONNX Runtime Web. Unlike most language models, where pre-training is based pri-marily on organic data sources such as web content or code, phi-4 strategically incorporates synthetic Model Card for UniXcoder-base Model Details Model Description UniXcoder is a unified cross-modal pre-trained model that leverages multimodal data (i. It was introduced in the paper Swin Transformer: Hierarchical Vision Transformer using Shifted Windows by Liu et al. microsoft/Phi-4-mini-instruct. ncbi/pubmed. Phi-4-mini brings significant enhancements in multilingual support, reasoning, and mathematics, and now, the long-awaited function calling feature is finally supported. Clone semantic kernel repository; Open your favorite IDE i. Fetching metadata from the HF Docker repository Swin Transformer (tiny-sized model) Swin Transformer model trained on ImageNet-1k at resolution 224x224. microsoft/bitnet-b1. License Orca 2 is licensed under the Microsoft Research License. 130 models. BitNet replaces traditional linear layers in Multi-Head Attention and feed-forward networks with specialized BitLinear layers. Simply enter text and include media URLs, and the system will handle the rest, providing re TrOCR (large-sized model, fine-tuned on IAM) TrOCR model fine-tuned on the IAM dataset. It was introduced in the paper TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models by Li et al. Try it out via this demo, or build and run it on your own CPU. 6B active parameters achieves a similar level of language understanding and math as much larger models. 2 models. 3 billion parameters, specialized for basic Python coding. Microsoft and Hugging Face are deepening their collaboration to bring over 11,000 open source and frontier models directly into Azure AI Foundry. SemanticKernel. VidTok is a cutting-edge family of video tokenizers that delivers state-of-the-art performance in both continuous and discrete tokenizations with various compression rates. code comment and AST) to pretrain code representation. Phi-4 Phi-4 is a state-of-the-art open model built upon a blend of synthetic datasets, data from filtered public domain websites, and acquired academic books and Q&A datasets. The simple unified architecture and training objectives make LayoutLMv3 a general-purpose pre-trained model. e: VSCode: Nov 7, 2024 · Databricks Runtime for Machine Learning includes Hugging Face transformers in Databricks Runtime 10. Zero-Shot Classification • Updated 21 days ago • 7 Upvote 59 +55; Share collection View history Repository: microsoft/orca-math-word-problems-200k; Paper: Orca-Math: Unlocking the potential of SLMs in Grade School Math; Direct Use This dataset has been designed to enhance the mathematical abilities of language models. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft’s Trademark & Brand Guidelines. The goal of this approach was to ensure that small capable models were trained with data focused on high quality and advanced reasoning. Introduction LayoutLMv2 is an improved version of LayoutLM with new pre-training tasks to model the interaction among text, layout, and image in a single multi-modal framework. 5-vision is a lightweight, state-of-the-art open multimodal model built upon datasets which include - synthetic data and filtered publicly available websites - with a focus on very high-quality, reasoning dense data both on text and vision. Document Image Transformer (base-sized model) Document Image Transformer (DiT) model pre-trained on IIT-CDIP (Lewis et al. Model card for RadEdit Model description RadEdit is a deep learning approach for stress testing biomedical vision models to discover failure cases. Dataset used to train microsoft/BioGPT-Large. 4 models. The large model pretrained on 16kHz sampled speech audio. Use this for deployment. 5 excels in two distinct yet cooperative transcription tasks: (1) generating spatially-aware text blocks, where each block of text is assigned its spatial coordinates within the image, and Microsoft Research Abstract We present phi-4, a 14-billion parameter language model developed with a training recipe that is centrally focused on data quality. How to track . Large Language and Vision Assistant for bioMedicine (i. May 21, 2024 · By combining Microsoft's robust cloud infrastructure with Hugging Face's most popular Large Language Models (LLMs), we are enhancing our copilot stacks to provide developers with advanced tools and models to deliver scalable, responsible, and safe generative AI solutions for custom business need. 5 models. 23 models. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. microsoft/Phi-3-mini-128k-instruct-onnx. I'm happy to help you with that. The model was pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. It was introduced in the paper Deep Residual Learning for Image Recognition by He et al. Hugging Face is the creator of Transformers, a widely popular library for working with over 200,000 open-source models hosted on the Hugging Face hub . Model Card for MAIRA-2 MAIRA-2 is a multimodal transformer designed for the generation of grounded or non-grounded radiology reports from chest X-rays. Model Summary Phi-3. May 4, 2023 · Hugging Face is a popular open-source platform for building and sharing state-of-the-art models in natural language processing. Feb 27, 2025 · These new Phi-4 mini and multimodal models are now available on Hugging Face, Azure AI Foundry Model Catalog, GitHub Models, and Ollama. Nov 10, 2023 · microsoft/Florence-2-base-ft. NOTE: This "delta model" cannot be used directly. Spaces using microsoft/CodeGPT-small-py 42. 151 models. E5-large News (May 2023): please switch to e5-large-v2, which has better performance and same method of usage. May 15, 2025 · A new era of AI . 📢 [GitHub Repo] [OmniParser V2 Blog Post] Huggingface demo. Oct 16, 2023 · Hi @Ashmit Gupta ,. Image-Text-to-Text • Updated Jul 20, 2024 • 82. Spaces using microsoft/speecht5_hifigan 100. Its training involved a variety of data sources, including subsets of Python codes from The Stack v1. The WhisperProcessor is used to:. 5: [mini-instruct]; [MoE-instruct]; [vision-instruct]. 5-MoE with only 6. All synthetic training data was moderated using the Microsoft Azure content filters. 🎉Phi-4: [multimodal-instruct | onnx]; [mini-instruct | onnx]. Please refer to LLaMA-2 technical report for details on the model architecture. The model is a vision backbone that can be plugged to other models for downstream tasks. Introduction LayoutXLM is a multimodal pre-trained model for multilingual document understanding, which aims to bridge the language barriers for visually-rich document understanding. Microsoft's WavLM. cpp) Model Variants Several versions of the model weights are available on Hugging Face: microsoft/bitnet-b1. The model is developed by Microsoft and is funded by Microsoft Research. May 23, 2023 · We’re excited to share that Microsoft has partnered with Hugging Face to bring open-source models to Azure Machine Learning. X-CLIP (base-sized model) X-CLIP model (base-sized, patch resolution of 32) trained fully-supervised on Kinetics-400. To persist the cache file on cluster termination, Databricks recommends changing the cache location to a Unity Catalog volume path by setting the environment variable HF_DATASETS_CACHE: Feb 26, 2025 · Copilot+ PCs will build upon Phi-4-multimodal’s capabilities, delivering the power of Microsoft’s advanced SLMs without the energy drain. Sharathhebbar24/One-stop Dec 2, 2021 · microsoft/LLM2CLIP-Llama3. It was introduced in the paper GIT: A Generative Image-to-text Transformer for Vision and Language by Wang et al. The default cache directory of datasets is ~/. , reference, synonym, contradiction) text . Model Details Architecture: Transformer-based, modified with BitLinear layers (BitNet framework). Hear from leaders at HF and Microsoft announcing a deeper partnership, and introducing exciting new solutions combining the best of Hugging Face state of the art ResNet-50 v1. 8B parameters with 6. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Table Transformer (fine-tuned for Table Detection) Table Transformer (DETR) model trained on PubTables1M. Model Details Model Description Microsoft's WavLM. It’s an update that sounds incremental on the surface. It uses a generative text-to-image model to “edit” chest X-rays by using a text description to add or remove abnormalities from a masked region of the image. 📰 Phi-4-mini Microsoft Blog 📖 Phi-4-mini Technical Report 👩‍🍳 Phi Cookbook 🏡 Phi Portal 🖥️ Try It Azure, Huggingface. import torch from transformers import AutoModel, AutoTokenizer # Load the model and tokenizer url = "microsoft/BiomedVLP-CXR-BERT-specialized" tokenizer = AutoTokenizer. Overall, Phi-3. 3 days ago · Today, Microsoft and Hugging Face are excited to announce an expanded collaboration that puts over ten thousand Hugging Face models at the fingertips of Azure developers. Model Summary OmniParser is a general screen parsing tool, which interprets/converts UI screenshot to structured format, to improve existing LLM based UI agent. Out-of-Scope Use TrOCR (large-sized model, fine-tuned on SROIE) TrOCR model fine-tuned on the SROIE dataset. Oct 16, 2024 · Learn why the Future of AI is: Model Choice . com) Tian Xie (tianxie@microsoft. 58-2B-4T-bf16: Contains the master weights in BF16 format. Fetching metadata from the HF Docker repository DeBERTa: Decoding-enhanced BERT with Disentangled Attention DeBERTa improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. Mar 21, 2024 · Microsoft. Org profile for Microsoft on Hugging Face, the AI community building the future. GIT. Nov 22, 2024 · microsoft/LLM2CLIP-Llama3. Model Summary The Phi-3-Mini-4K-Instruct is a 3. 7 billion parameters. DeBERTa: Decoding-enhanced BERT with Disentangled Attention DeBERTa improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. This project may contain trademarks or logos for projects, products, or services. Aurora: A Foundation Model for the Earth System This repository contains model weights for various versions of Aurora. Kosmos-2: Grounding Multimodal Large Language Models to the World [An image of a snowman warming himself by a fire. The model is a mixture-of-expert decoder-only Transformer model using the tokenizer with vocabulary size of 32,064. Model Summary The language model Phi-1 is a Transformer with 1. However, most pretraining efforts focus on general domain corpora, such as newswire and Web. Text Embeddings by Weakly-Supervised Contrastive Pre-training. Language models are available in short- and long-context lengths. Hosting over 200,000 open-source models , and serving over 1 million model downloads a day, Hugging Face is the go-to destination for all of Machine May 24, 2022 · Hugging Face (HF), the leading open-source platform for data scientists and Machine Learning (ML) practitioners, is working closely with Microsoft to democratize responsible machine learning through open source and open collaboration. 0% few-shot accuracy on MATH dataset, respectively — matching DeepSeekMath with only 3% of the pretraining tokens. 📢 [Project Page] [] [] Model Summary OmniParser is a general screen parsing tool, which interprets/converts UI screenshot to structured format, to improve existing LLM based UI agent. cpp library for CPU inference. ] This Hub repository contains a HuggingFace's transformers implementation of the original Kosmos-2 model from Microsoft. Model tree for microsoft/git-base-coco. One year ago, Microsoft introduced small language models (SLMs) to customers with the release of Phi-3 on Azure AI Foundry, leveraging research on SLMs to expand the range of efficient AI models and tools available to customers. Any use of third-party trademarks or logos are subject to those third-party’s policies. It was trained using the same data sources as Phi-1. TrOCR (base-sized model, fine-tuned on SROIE) TrOCR model fine-tuned on the SROIE dataset. May 1, 2025 · Developers: Microsoft Research: Description: Phi-4-reasoning-plus is a state-of-the-art open-weight reasoning model finetuned from Phi-4 using supervised fine-tuning on a dataset of chain-of-thought traces and reinforcement learning. May 21, 2024 · Hugging Face and Microsoft have been collaborating for 3 years to make it easy to export and use Hugging Face models with ONNX Runtime, through the optimum open source library. 6k • 113 Upvote 168 +164; Share collection View history Kosmos-2. BioGPT Pre-trained language models have attracted increasing attention in the biomedical domain, inspired by their great success in the general natural language domain. 1 model. and first released in this repository. Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks Model Summary This Hub repository contains a HuggingFace's transformers implementation of Florence-2 model from Microsoft. 58-2B-4T-gguf: Contains the model weights in GGUF format, compatible with the bitnet. 05k TrOCR (base-sized model, fine-tuned on IAM) TrOCR model fine-tuned on the IAM dataset. Phi-3 family of small language and multi-modal models. More details about the model can be found in the Orca 2 paper. cache/huggingface/datasets. Phi-2 is a Transformer with 2. e. bitnet. Azure AI Foundry Models now allows immediate deployment of the most popular open models on Hugging Face, spanning text, vision, speech, and multimodal models. cpp is the official inference framework for 1-bit LLMs (e. Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks Model Summary This is a continued pretrained version of Florence-2-large model with 4k context length, only 0. Updated Jan 26, 2024 • 706 • 127 Spaces using microsoft/BioGPT-Large 41. 5 SLMs a Game-Change May 9, 2025 · Phi-4 Available on HuggingFace: A Big Thanks to How to Fine-Tune Phi-4 Locally? Phi 3 – Small Yet Powerful Models from Mi Microsoft Phi-4 Multimodal: Hands-on Guide. Model tree for microsoft/speecht5_hifigan. Finetunes. , 2006), a dataset that includes 42 million document images. 1B samples are used for continue pretraining, thus it might not be trained well. Pre-process the audio inputs (converting them to log-Mel spectrograms for the model) SpeechT5 (TTS task) SpeechT5 model fine-tuned for speech synthesis (text-to-speech) on LibriTTS. Solving complicated AI tasks with different domains and modalities is a key step toward artificial general intelligence. BitNet. 5 ResNet model pre-trained on ImageNet-1k at resolution 224x224. 😻. Pre-trained on large-scale text-intensive images, Kosmos-2. The model is shared by Microsoft Research and is licensed under the MIT License. 1 day ago · At Microsoft Build 2025, Satya Nadella took to the stage with a familiar partner, but under a much-expanded vision. LLaVA-Med v1. Spaces using microsoft/deberta-v3-large 42. CL} } VidTok A Family of Versatile and State-Of-The-Art Video Tokenizers. Some potential uses are: May 1, 2025 · Microsoft Research: Description: Phi-4-reasoning is a state-of-the-art open-weight reasoning model finetuned from Phi-4 using supervised fine-tuning on a dataset of chain-of-thought traces and reinforcement learning. Experiment results show that it has significantly outperformed the existing SOTA cross-lingual pre-trained models on the XFUND dataset. , “LLaVA-Med”) is a large language and vision model trained using a curriculum learning method for adapting LLaVA to the biomedical domain. SpeechT5 (voice conversion task) SpeechT5 model fine-tuned for voice conversion (speech-to-speech) on CMU ARCTIC. Apr 15, 2025 · We’re on a journey to advance and democratize artificial intelligence through open source and open science. Developed by: Microsoft Health Futures; Model type: Vision transformer; License: MSRLA; Finetuned from model: dinov2-base; Uses RAD-DINO is shared for research purposes only. 5-turbo-0301. 58-bit models on CPU (with NPU and GPU support coming next). Please see the GitHub repository for more information. 🎉 Phi-3. The base model pretrained on 16kHz sampled speech audio. The Semantic Kernel API, on the other hand, is a powerful tool that allows developers to perform various NLP tasks, such as text classification and entity recognition, using pre-trained models. 5-mini is a lightweight, state-of-the-art open model built upon datasets used for Phi-3 - synthetic data and filtered publicly available websites - with a focus on very high-quality, reasoning dense data. 3 models. Public repo for HF blog posts. 8B parameters, lightweight, state-of-the-art open model trained with the Phi-3 datasets that includes both synthetic data and the filtered publicly available websites data with a focus on high-quality and reasoning dense properties. g. Dataset used to train microsoft/swinv2-tiny-patch4-window8-256. Return only the result in the format '[x_min, y_min, x_max, y_max]', where (x_min, y_min) represents the coordinates of the top-left corner of the bounding box, and (x_max, y_max) represents the coordinates of the bottom-right corner. . This integration will enhance productivity, creativity, and education-focused experiences, becoming a standard part of our developer platform. Collection GIT (Generative Image-to-text Transformer) is a May 1, 2025 · This project may contain trademarks or logos for projects, products, or services. Could you help me identify the location of scale on the geologic map? The input image dimensions are (width, height) = (2164, 2380). Microsoft has partnered with Hugging Face to bring open-source models from Hugging Face Hub to Azure Machine Learning. Recently, Hugging Face and Microsoft have been focusing on enabling local inference through WebGPU, leveraging Transformers. 4 LTS ML and above, and includes Hugging Face datasets, accelerate, and evaluate in Databricks Runtime 13. 1-8B-siglip2-so400m-patch14-224. SemanticKernel; Microsoft. Text Generation • Updated Feb 29, 2024 • 352k We’re on a journey to advance and democratize artificial intelligence through open source and open science. this model is identical to microsoft/phi-4 this was reuploaded from azure before microsoft uploaded the model to huggingface themselves. Microsoft Launches Two Powerful Phi-4 Reasoning Phi-2 Unleashed: Language Models with Compact B What Makes Microsoft Phi 3. Table Transformer (fine-tuned for Table Structure Recognition) Table Transformer (DETR) model trained on PubTables1M. Apr 22, 2024 · Phi-3 family of small language and multi-modal models. Text Generation • Updated May 22, 2024 • 156 • 188 microsoft/Phi-3-medium-4k-instruct ️ Official Inference Code: microsoft/BitNet (bitnet. from_pretrained(url, trust_remote_code= True) # Input text prompts (e. 58). com) Downloads last month-Downloads are not tracked for this model. Text Generation • Updated 20 days ago • 435k • 478 microsoft/phi-4. , 2006), a dataset that includes 42 million document images and fine-tuned on RVL-CDIP, a dataset consisting of 400,000 grayscale images in 16 classes, with 25,000 images per class. from_pretrained(url, trust_remote_code= True) model = AutoModel. zevzpqa owrysme ndk iktze xddr oco xncpy rhvl bwadx iehqkv