# Transformers Transformers is a model-definition framework for machine-learning models in text, computer vision, audio, video, and multimodal applications, supporting inference and training. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Agentic Engineering in Python: From Vibes to Evidence DevFeed: [Agentic Engineering in Python: From Vibes to Evidence]() Original publisher: [Read original article]() Author: Ben Batman Published: 2026-09-14T14:00:00Z Content type: tutorial Language: en Sources: [Real Python]() Topics: [Loop Engineering](), [AI Agent](), [Python](), [Transformers](), [Code review](), [Test-driven development]() Tags: [agentic](), [agentic-engineering](), [ai-coding-agents](), [cheat-sheet](), [code-review](), [learning](), [python](), [tests]() ### AI overview This tutorial presents agentic engineering in Python as a disciplined workflow for delegating bounded tasks to AI coding agents while retaining human control over intent, constraints, review, and release decisions. It explains execution and acceptance loops, the Engineering Evidence Ladder, and the RECAP review method for evaluating agent-generated changes with tests, type hints, refactoring, and code review. ### Source excerpt Move from vibe coding to agentic engineering in Python, using tests, types, and code review to prove an AI agent's changes are safe to keep. ## Открываем претрейн Alice AI Search: как устроена модель быстрых ответов Алисы на Поиске DevFeed: [Открываем претрейн Alice AI Search: как устроена модель быстрых ответов Алисы на Поиске]() Original publisher: [Read original article]() Author: pet67 (Яндекс) Published: 2026-09-11T06:05:13Z Content type: article Language: ru Sources: [Яндекс - Как мы делаем Яндекс / Статьи]() Topics: [Artificial Intelligence](), [AI search](), [Transformers](), [Mixture of Experts (MoE)](), [Large Language Model](), [hugging face]() Tags: [agentic](), [ai](), [ai-search](), [alice-ai](), [hugging-face](), [llm](), [ml](), [moe](), [rl](), [tag-178bc8f01f24](), [tag-4004cf5948d3](), [tag-61cd5a476b1d](), [tag-d89cae10e887](), [tag-e6d9cc1f0757](), [transformers]() ### AI overview This developer article explains the Alice AI Search pipeline for generating fast answers, including its search and context-processing stages, shorter information contexts, a sparse Mixture-of-Experts architecture combined with an Encoder-Decoder, and online reinforcement learning from user behavior signals. It also announces the open release of the Alice AI-T5-35B-A0.6B Base model, with external inference available through Hugging Face Transformers while optimized production inference remains internal to Yandex. ### Source excerpt Быстрый ответ Алисы AI -- это самый массовый генеративный продукт Яндекса и первое соприкосновение с Алисой для пользователей Поиска. Даже в час пиковой нагрузки пользователь должен получить лаконичный ответ за считаные секунды. Для этого мы, команда Alice AI Search, адаптируем весь пайплайн быстрых ответов -- от собственного претрейна с кастомной архитектурой до онлайн-rl-обучения на поведенческие сигналы пользователей. В статье разберём, как устроен генеративный ответ в Поиске, и расскажем про основные улучшения июньского релиза: как мы ускорили ответы за счёт коротких инфоконтекстов, зачем совместили Encoder-Decoder с разреженной MoE-архитектурой и как обучение на реальных пользовательских сигналах повлияло на качество и использование продукта. Кроме того, мы выложили в открытый доступ обученную с нуля модель Alice AI-T5-35B-A0.6B Base с тем ограничением, что внешним пользователям доступен инференс через Hugging Face Transformers, а оптимизированный production-инференс пока доступен только внутри Яндекса. Читать далее ## This Week In AI Research (🗓 9-16 July 26) DevFeed: [This Week In AI Research (🗓 9-16 July 26)]() Original publisher: [Read original article]() Author: Dr. Ashish Bamania Published: 2026-07-22T09:14:26Z Content type: article Language: en Sources: [Into AI]() Topics: [Artificial Intelligence](), [AI Research](), [releases](), [Large Language Model](), [Mixture of Experts (MoE)](), [long-context](), [Open Source Models & Datasets](), [Transformers]() Tags: [ai](), [architecture](), [benchmarks](), [context-window](), [llms](), [models](), [moe](), [open](), [releases](), [research](), [transformers]() ### AI overview A weekly roundup of AI research papers and releases, covering Kimi K3, Expanded Hyper-Connections, VideoChat3, and other developments. It describes Kimi K3's architecture, context window, benchmark performance, and limitations, and summarizes xHC's reported efficiency improvements. ### Source excerpt The top 10 AI research papers and releases this week (Kimi K3, Inkling, WanSong v1.0, Bonsai 27B, and many more) ## Everything a Senior Engineer Needs to Know About What's Inside an LLM DevFeed: [Everything a Senior Engineer Needs to Know About What's Inside an LLM]() Original publisher: [Read original article]() Author: Sidwyn Koh Published: 2026-06-20T17:00:09Z Content type: tutorial Language: en Sources: [Path to Staff]() Topics: [Large Language Model](), [model architecture](), [Neural Network](), [Transformers]() Tags: [architecture](), [learning](), [llms](), [models](), [neural-network]() ### AI overview This tutorial explains the architecture and internal components of large language models, including the role of neural networks, recurrent neural networks, transformers, and diffusion models. It is part of a series covering AI systems in depth. ### Source excerpt Learn what AI models are made of ## Transformers v5: Simple model definitions powering the AI ecosystem DevFeed: [Transformers v5: Simple model definitions powering the AI ecosystem]() Original publisher: [Read original article]() Author: Lysandre; Arthur Zucker; Cyril Vallez; Vaibhav Srivastav Published: 2025-12-01T00:00:00Z Content type: release Language: en Sources: [Hugging Face - Blog]() Topics: [Transformers](), [hugging face](), [model architecture](), [Artificial Intelligence](), [Open Source]() Tags: [ai](), [announcement](), [community](), [hugging-face](), [inference](), [model-architecture](), [open-source](), [python](), [transformers](), [v5]() ### AI overview Hugging Face announces Transformers v5, focusing on simpler model definitions and improvements for training, inference, and production. The article describes the library's growth to more than 400 model architectures and over 1.2 billion installs, along with modular design intended to improve maintenance, integration, and collaboration. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Link Graveyard: A snapshot of my abandoned browser tabs DevFeed: [Link Graveyard: A snapshot of my abandoned browser tabs]() Original publisher: [Read original article]() Published: 2025-09-13T00:00:00Z Content type: article Language: en Sources: [Tim Kellogg]() Topics: [Artificial Intelligence](), [Large Language Model](), [Transformers](), [Graphs](), [Open Source Models & Datasets](), [Meta]() Tags: [ai](), [browser](), [graph](), [llms](), [meta](), [open-source](), [snapshot](), [transformers]() ### AI overview A personal roundup of abandoned browser tabs, briefly annotated with comments on AI research, language models, agentic and coding abilities, datasets, AI consciousness, and related writing. ### Source excerpt A dump of all my browser tabs on my phone, briefly annotated ## Tricks from OpenAI gpt-oss YOU 🫵 can use with transformers DevFeed: [Tricks from OpenAI gpt-oss YOU 🫵 can use with transformers]() Original publisher: [Read original article]() Author: Aritra Roy Gosthipaty; Sergio Paniego; Vaibhav Srivastav; Pedro Cuenca; Arthur Zucker; Nathan Habib; Cyril Vallez Published: 2025-09-11T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Transformers](), [gpt-oss](), [Mixture of Experts (MoE)](), [CUDA](), [Large Language Model](), [OpenAI](), [PyTorch](), [Open Source](), [C++](), [Python]() Tags: [c-plus-plus](), [cuda](), [gpt-oss](), [guide](), [llms](), [mixture-of-experts-moe](), [open-source](), [openai](), [optimization](), [python](), [pytorch](), [transformers]() ### AI overview This article explains performance improvements in the Transformers toolkit, including specialized kernels, kernel fusion, Flash Attention, and pre-built kernel binaries. It highlights how GPT-OSS, a Mixture of Experts model, uses custom kernels distributed through the Hub. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Welcome GPT OSS, the new open-source model family from OpenAI! DevFeed: [Welcome GPT OSS, the new open-source model family from OpenAI!]() Original publisher: [Read original article]() Author: Vaibhav Srivastav; Pedro Cuenca; Lewis Tunstall; Clem 🤗; Matthew Carrigan; Clémentine Fourrier; Célina Hanouti; Lucain Pouget; Marc Sun; Simon Pagezy Published: 2025-08-05T00:00:00Z Content type: release Language: en Sources: [Hugging Face - Blog]() Topics: [gpt-oss](), [Open Source](), [OpenAI](), [Artificial Intelligence](), [inference-providers](), [llama.cpp](), [moe](), [Ollama](), [Python](), [quantization](), [Transformers](), [vllm]() Tags: [api](), [artificial-intelligence](), [community](), [gpt](), [gpt-oss](), [inference-providers](), [javascript](), [llama-cpp](), [llm](), [moe](), [ollama](), [open-source](), [openai](), [python](), [quantization](), [transformers](), [vllm]() ### AI overview Hugging Face welcomes OpenAI's gpt-oss open-source model family. The article describes the models' Apache 2.0 licensing, local deployment options, reasoning and tool-use capabilities, MoE architecture, quantization, supported inference implementations, and access through Inference Providers and the Responses API. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Holo1: New family of GUI automation VLMs powering GUI agent Surfer-H DevFeed: [Holo1: New family of GUI automation VLMs powering GUI agent Surfer-H]() Original publisher: [Read original article]() Author: Mats L Richter; Pierre-Louis Cedoz Published: 2025-06-03T13:27:59Z Content type: release Language: en Sources: [Hugging Face - Blog]() Topics: [Artificial Intelligence](), [computer-use](), [browsers](), [Website](), [Benchmark](), [Transformers](), [hugging face](), [Localization (l10n)]() Tags: [agents](), [ai](), [automation](), [benchmarks](), [browsers](), [cost](), [gui](), [models](), [modular](), [open-source](), [performance](), [transformers](), [vlms](), [web]() ### AI overview Holo1 is an open-source family of Action VLMs for understanding web interfaces and locating UI elements precisely. Holo1-3B and Holo1-7B are released on Hugging Face with the WebClick benchmark; Holo1-7B reports 76.2% average accuracy on common UI localization benchmarks. The article also describes Surfer-H, a browser-based web automation agent built with separate policy, localization, and validation components. ### Source excerpt Surfer-H, a web-native agent that interacts with browsers like a human relies on the Holo1. Holo1 is the first family of open-source Action VLMs designed specifically for deep web UI understanding and precise localization. The family includes Holo1-3B and Holo1-7B models, with the latter achieving 76.2% average accuracy on common UI localization benchmarks--the highest among small-size models. ## Welcome Llama 4 Maverick & Scout on Hugging Face DevFeed: [Welcome Llama 4 Maverick & Scout on Hugging Face]() Original publisher: [Read original article]() Author: ben burtenshaw; Vaibhav Srivastav; Pedro Cuenca; Clem 🤗; Rajat Arya; Jared Sulzdorf; Lysandre Published: 2025-04-05T00:00:00Z Content type: release Language: en Sources: [Hugging Face - Blog]() Topics: [llama](), [hugging face](), [Mixture of Experts (MoE)](), [multimodal](), [Transformers](), [tgi](), [quantization](), [Fine-tuning](), [model-deployment]() Tags: [fine-tuning](), [hugging-face](), [inference](), [llama](), [meta](), [mixture-of-experts-moe](), [multimodal](), [quantization](), [tgi](), [transformers]() ### AI overview Hugging Face announces the release of Meta's Llama 4 Maverick and Scout, natively multimodal models built with an autoregressive Mixture-of-Experts architecture. The article describes their parameter sizes, expert counts, training data, language support, quantization options, licensing, and integration with the Hugging Face Hub, Transformers, TRL, and TGI. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## The NLP Course is becoming the LLM Course DevFeed: [The NLP Course is becoming the LLM Course]() Original publisher: [Read original article]() Author: ben burtenshaw; Vaibhav Srivastav; Lewis Tunstall; Florent Daudens; Pedro Cuenca; Tom Aarsen; Eliott Coyac; Mishig ᠮᠢᠰᠾᠢᠭ; Sergio Paniego; Julien Chaumond Published: 2025-04-03T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [LLMs](), [hugging face](), [Natural language processing](), [Fine-tuning](), [Inference](), [Transformers](), [spaces](), [sentence-transformers]() Tags: [education](), [fine-tuning](), [inference](), [llm-course](), [nlp](), [nlp-course](), [open-source](), [sentence-transformers](), [spaces](), [transformers]() ### AI overview Hugging Face is renaming and expanding its NLP course as The LLM course. The refreshed curriculum adds material on fine-tuning LLMs, reasoning models, inference, retrieval, and modern NLP methods while retaining foundational tasks such as classification and named entity recognition. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Finally, a Replacement for BERT: Introducing ModernBERT DevFeed: [Finally, a Replacement for BERT: Introducing ModernBERT]() Original publisher: [Read original article]() Author: Benjamin Warner; Antoine Chaffin; Benjamin Clavié; Orion Weller; Oskar Hallström; Said Taghadouini; Alexis Gallagher; Raja Biswas; Faisal Ladhak; Tom Aarsen Published: 2024-12-19T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Machine learning](), [Transformers](), [Retrieval-Augmented Generation](), [recommendation systems](), [PyTorch]() Tags: [ai](), [community](), [fine-tuning](), [flash-attention-2](), [gpu](), [language-models](), [nlp](), [open-source-collab](), [performance](), [rag](), [research](), [transformers]() ### AI overview The article introduces ModernBERT, a family of encoder-only models designed as improved replacements for BERT-like models. It describes 8,192-token context, stronger downstream performance, faster processing, base and large model sizes, compatibility with Transformers, and use cases including retrieval, classification, question answering, entity extraction, RAG pipelines, and recommendation systems. It also recommends Flash Attention 2 when supported by the GPU. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## SmolVLM - small yet mighty Vision Language Model DevFeed: [SmolVLM - small yet mighty Vision Language Model]() Original publisher: [Read original article]() Author: Andres Marafioti; merve; Miquel Farré; Elie Bakouch; Pedro Cuenca Published: 2024-11-26T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [smolvlm](), [vlm](), [multimodal-ai](), [Transformers](), [Fine-tuning](), [Inference](), [Open Source Models & Datasets](), [GPU]() Tags: [apache](), [fine-tuning](), [gpu](), [inference](), [llm](), [multimodal](), [multimodal-ai](), [nlp](), [on-device](), [open](), [open-source](), [smolvlm](), [transformers](), [trl](), [vision]() ### AI overview This article introduces SmolVLM, a family of small, fast, memory-efficient 2B vision-language models released fully open source under the Apache 2.0 license. It describes the model variants, architecture, training resources, Transformers integration, demo, fine-tuning script, and efficient local or on-device deployment. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Faster Text Generation with Self-Speculative Decoding DevFeed: [Faster Text Generation with Self-Speculative Decoding]() Original publisher: [Read original article]() Author: Aritra Roy Gosthipaty; Mostafa Elhoushi; Pedro Cuenca; Vaibhav Srivastav Published: 2024-11-20T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [text-generation](), [Inference](), [Transformers](), [Latency](), [Deployment](), [Hardware]() Tags: [benchmark](), [collaboration](), [fine-tuning](), [hardware](), [hugging-face](), [inference](), [latency](), [nlp](), [open-source](), [research](), [text-generation](), [transformers]() ### AI overview This article explains self-speculative decoding, a technique that uses the early layers of a large language model to generate draft tokens and its deeper layers to verify them. With specialized training, the approach can accelerate text generation, reduce computational costs and latency, save memory, and enable inference on smaller GPUs. The article also demonstrates the method with the transformers library, code examples, benchmarks, and practical deployment guidance. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Introducing HUGS - Scale your AI with Open Models DevFeed: [Introducing HUGS - Scale your AI with Open Models]() Original publisher: [Read original article]() Author: Philipp Schmid; Jeff Boudier; Alvaro Bartolome; Simon Pagezy; Violette Published: 2024-10-23T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [hugging face](), [model-deployment](), [tgi](), [Inference](), [Transformers](), [Open Source](), [Generative AI](), [Large Language Model](), [GPU](), [Microservices](), [API](), [Hardware]() Tags: [accelerate](), [accelerators](), [ai](), [ai-accelerator](), [amd](), [announcement](), [api](), [artificial-intelligence](), [aws](), [aws-inferentia](), [azure](), [digitalocean](), [enterprise](), [gcp](), [gpu](), [inference](), [llm](), [microservices](), [model-deployment](), [models](), [multimodal](), [open-source](), [partnerships](), [performance](), [technologies](), [text-generation](), [tgi](), [transformers]() ### AI overview Hugging Face introduces HUGS, optimized zero-configuration inference microservices for deploying open models in an organization's own infrastructure. Built on Hugging Face technologies including Text Generation Inference and Transformers, HUGS targets efficient, hardware-optimized deployment across NVIDIA and AMD GPUs, with AWS Inferentia and Google TPU support planned. It provides an OpenAI-compatible API and is designed to reduce deployment complexity and time for AI applications. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Fixing Gradient Accumulation DevFeed: [Fixing Gradient Accumulation]() Original publisher: [Read original article]() Author: Lysandre; Arthur Zucker; Zachary Mueller; Yih-Dar SHIEH; Benjamin Bossan; Pedro Cuenca Published: 2024-10-16T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [gradient\_accumulation](), [Transformers]() Tags: [bug](), [gradient-accumulation](), [training](), [transformers]() ### AI overview The article explains a bug in gradient accumulation for token-level tasks, where averaging per-batch losses produced results that did not match full-batch training. It describes a fix that accounts for the total number of non-padding tokens and introduces an API for supplying custom loss functions. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Faster Assisted Generation with Dynamic Speculation DevFeed: [Faster Assisted Generation with Dynamic Speculation]() Original publisher: [Read original article]() Author: Jonathan Mamou; Oren Pereg; Joao Gante; Lewis Tunstall; Daniel Korat; Nadav Timor; Moshe Wasserblat Published: 2024-10-08T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [AI Inference](), [Inference](), [large-language-models](), [Transformers](), [Optimization](), [Latency]() Tags: [inference](), [language-models](), [large-language-models](), [latency](), [nlp](), [optimization](), [research](), [tokens](), [transformers]() ### AI overview The article explains dynamic speculation lookahead for accelerating large language model inference through speculative decoding. A fast draft model generates candidate tokens, while a larger target model verifies them in parallel. It presents an oracle-based method that identifies the maximum number of draft tokens accepted at each iteration, aiming to reduce inference latency beyond static and heuristic schedules. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Llama can now see and run on your device - welcome Llama 3.2 DevFeed: [Llama can now see and run on your device - welcome Llama 3.2]() Original publisher: [Read original article]() Author: merve; Philipp Schmid; Omar Sanseviero; Vaibhav Srivastav; Lewis Tunstall; Aritra Roy Gosthipaty; Pedro Cuenca Published: 2024-09-25T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [llama](), [multimodal](), [Meta](), [hugging face](), [Inference](), [Large Language Model](), [model-deployment](), [Open Source](), [Fine-tuning](), [inference-endpoints](), [tgi](), [Transformers]() Tags: [artificial-intelligence](), [fine-tuning](), [gpu](), [hugging-face](), [inference](), [inference-endpoints](), [llama](), [llm](), [llms](), [meta](), [model](), [models](), [multimodal](), [nlp](), [on-device](), [open-source](), [reasoning](), [retrieval](), [tgi](), [transformers](), [vision]() ### AI overview Meta's Llama 3.2 release introduces multimodal Vision models in 11B and 90B sizes, smaller text-only 1B and 3B models for on-device use, and vision-enabled Llama Guard 3. The article describes their capabilities, architecture, supported languages, inference examples, and integrations with Hugging Face Transformers, TGI, Inference Endpoints, Google Cloud, Amazon SageMaker, and DELL Enterprise Hub. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Improving Hugging Face Training Efficiency Through Packing with Flash Attention 2 DevFeed: [Improving Hugging Face Training Efficiency Through Packing with Flash Attention 2]() Original publisher: [Read original article]() Author: Rhui Dih Lee; Arthur Zucker; Achintya Kundu; Laura Wynter; Raghu Ganti; Mayank Mishra Published: 2024-08-21T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [hugging face](), [Transformers](), [Machine learning](), [GPU](), [datasets]() Tags: [datasets](), [efficiency](), [flash-attention-2](), [gpu](), [hugging-face](), [models](), [packing](), [padding](), [training](), [transformers]() ### AI overview The article explains how Hugging Face improves training throughput by packing instruction-tuning sequences without padding while using Flash Attention 2. A new data collator preserves sequence boundaries and can provide up to 2x higher throughput while maintaining convergence quality, with measured gains varying by dataset and sequence-length variance. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Announcing New Hugging Face and KerasHub integration DevFeed: [Announcing New Hugging Face and KerasHub integration]() Original publisher: [Read original article]() Author: Aritra Roy Gosthipaty Published: 2024-07-10T00:00:00Z Content type: news Language: en Sources: [Hugging Face - Blog]() Topics: [hugging face](), [Keras](), [Transformers](), [gemma](), [llama](), [Open Source]() Tags: [gemma](), [hugging-face](), [json](), [keras](), [llama](), [nlp](), [open-source](), [open-source-collab](), [pytorch](), [tensorflow](), [transformers]() ### AI overview The article announces an integration between Hugging Face Transformers and KerasHub through a shared model save format. It allows KerasHub users to load many Transformers checkpoints, initially including Gemma, Llama 3, and PaliGemma, and use them with TensorFlow, JAX, or PyTorch backends. The integration handles conversion of configuration variables, weight names, and tokenizer vocabularies internally. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Our Transformers Code Agent beats the GAIA benchmark 🏅 DevFeed: [Our Transformers Code Agent beats the GAIA benchmark 🏅]() Original publisher: [Read original article]() Author: Aymeric Roucher; Sergei Petrov Published: 2024-07-01T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [gaia](), [Benchmark](), [Transformers](), [smolagents](), [Large Language Model](), [Graphs](), [Retrieval-Augmented Generation]() Tags: [agent](), [agents](), [artificial-intelligence](), [benchmark](), [code](), [community](), [gaia](), [leaderboard](), [multimodal](), [nlp](), [research](), [retrieval-augmented-generation](), [smolagents](), [tool](), [tools](), [transformers]() ### AI overview The article presents a Transformers Code Agent that achieved top performance on the GAIA benchmark. It explains how LLM-based agents use tools and dynamically change their execution graph, and notes that the framework has since been upgraded to the standalone smolagents library. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Introducing the Hugging Face Embedding Container for Amazon SageMaker DevFeed: [Introducing the Hugging Face Embedding Container for Amazon SageMaker]() Original publisher: [Read original article]() Author: Philipp Schmid; Jeff Boudier Published: 2024-06-07T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [model-deployment](), [Amazon SageMaker](), [Amazon Bedrock](), [Inference](), [Embeddings](), [Transformers](), [sentence-transformers](), [AWS IAM]() Tags: [amazon-sagemaker](), [aws](), [batching](), [cloud](), [container](), [embedding](), [embeddings](), [gpu](), [guide](), [hugging-face](), [inference](), [partnership](), [prometheus](), [sdk](), [sentence-transformers](), [telemetry](), [tracing]() ### AI overview This tutorial explains how to deploy open embedding models to Amazon SageMaker using the Hugging Face Embedding Container. It uses Text Embeddings Inference for efficient, production-ready serving and covers container selection, CPU and GPU variants, batching, and observability features. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Hugging Face redesigns Transformers documentation for developers building AI products DevFeed: [Hugging Face redesigns Transformers documentation for developers building AI products]() Original publisher: [Read original article]() Author: Steven Liu Published: 2024-06-07T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Transformers](), [Documentation](), [hugging face](), [AI Development]() Tags: [community](), [developers](), [documentation](), [how-to](), [hugging-face](), [open-source](), [transformers]() ### AI overview Hugging Face describes a redesign of the Transformers documentation to address its disjointed structure and make it more useful for developers building AI products. The redesign emphasizes code-first, solution-oriented content, integrated explanations of beginner machine learning concepts, and progressive learning. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## PaliGemma - Google's Cutting-Edge Open Vision Language Model DevFeed: [PaliGemma - Google's Cutting-Edge Open Vision Language Model]() Original publisher: [Read original article]() Author: merve; Andreas P. Steiner; Pedro Cuenca Published: 2024-05-14T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [vlm](), [Google](), [Transformers](), [hugging face](), [Open Source](), [Inference](), [Human-AI evaluation](), [smolvlm]() Tags: [artificial-intelligence](), [google](), [hugging-face](), [inference](), [llm](), [model](), [multimodal](), [ocr](), [open-source](), [transformers](), [vision]() ### AI overview PaliGemma is a family of open vision-language models from Google that accepts images and text and produces text. The article describes its SigLIP image encoder, Gemma text decoder, model variants, resolutions, precisions, Transformers integration, and use for tasks such as captioning, referring segmentation, and OCR. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. [Next page]()