# vlm

Published articles for vlm.

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## Building Pinterest's VLM Serving Stack on NVIDIA Dynamo

DevFeed: [Building Pinterest's VLM Serving Stack on NVIDIA Dynamo](<https://devfeed.tech/articles/building-pinterest-s-vlm-serving-stack-on-nvidia-dynamo-1229.md>)

Original publisher: [Read original article](<https://medium.com/pinterest-engineering/building-pinterests-vlm-serving-stack-on-nvidia-dynamo-0dce6e93d0f3?source=rss----4c5a5f6279b6---4>)

Author: Pinterest Engineering

Published: 2026-09-10T23:08:16Z

Content type: article

Language: en

Sources: [Pinterest Engineering Blog - Medium](<https://devfeed.tech/sources/pinterest-engineering-blog-medium.md>)

Topics: [vlm](<https://devfeed.tech/topics/vlm.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [model-deployment](<https://devfeed.tech/topics/model-deployment.md>), [Orchestration](<https://devfeed.tech/topics/orchestration.md>), [Latency](<https://devfeed.tech/topics/latency.md>), [Caching](<https://devfeed.tech/topics/caching.md>), [Open Source Models & Datasets](<https://devfeed.tech/topics/open-source-models-datasets.md>)

Tags: [blackwell](<https://devfeed.tech/tags/blackwell.md>), [cache](<https://devfeed.tech/tags/cache.md>), [dynamo](<https://devfeed.tech/tags/dynamo.md>), [embeddings](<https://devfeed.tech/tags/embeddings.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [inference](<https://devfeed.tech/tags/inference.md>), [latency](<https://devfeed.tech/tags/latency.md>), [llm](<https://devfeed.tech/tags/llm.md>), [multimodal](<https://devfeed.tech/tags/multimodal.md>), [multimodal-ai](<https://devfeed.tech/tags/multimodal-ai.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [nvidia-blackwell](<https://devfeed.tech/tags/nvidia-blackwell.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [orchestration](<https://devfeed.tech/tags/orchestration.md>), [performance](<https://devfeed.tech/tags/performance.md>), [pinterest](<https://devfeed.tech/tags/pinterest.md>), [vllm](<https://devfeed.tech/tags/vllm.md>), [vlm](<https://devfeed.tech/tags/vlm.md>), [vlm-serving](<https://devfeed.tech/tags/vlm-serving.md>)

### AI overview

Pinterest describes its VLM serving stack built on NVIDIA Blackwell GPUs and NVIDIA Dynamo. The stack addresses multimodal inference demands such as image processing, variable prefill costs, KV-cache pressure, routing, and cache offloading.

### Source excerpt

Lei Pan | Senior Software Engineer; Salina Wu | Senior Software Engineer; Cristian Lopez | Software Engineer I; Guangtong Bai | Staff Software Engineer; Soam Acharya | Principal Engineer; Saurabh Vishwas Joshi | Principal Engineer; Chia-Wei Chen | Staff Software Engineer; Ambud Sharma | Principal Engineer Why VLM Serving Matters at Pinterest Pinterest is a visual search and discovery platform, so its AI systems must reason over both language and visual content. Vision-language models (VLMs), which can interpret images, compare visual candidates, and respond naturally to user intent, are becoming the foundation for the next generation of Pinterest experiences: Pinterest Assistant, hybrid search, multimodal reranking, content understanding, signal generation, content safety, and more. This direction also reflects Pinterest's broader strategy to customize open-source models to meet its product & scale needs. Pinterest Assistant is a standout example. This multi-turn conversational experience covers both user language and visual content. Serving it requires low-latency VLM inference over rich multimodal context as well as reworking Qwen3-VL with proprietary multimodal embeddings to cut runtime cost while improving performance. Serving VLMs, however, introduces more challenges compared to text-only LLM workloads. Requests may carry multiple images, require extra vision encoder computation, incur larger and more variable prefill cost, and create higher KV cache pressure. To support this new class of models & product experiences, we built Pinterest's VLM serving stack on top of NVIDIA Blackwell GPUs and NVIDIA Dynamo. Blackwell GPUs incorporate many architectural innovations that are uniquely positioned for today's most demanding AI workloads -- including higher BF16/FP8 compute throughput, increased memory bandwidth, and larger HBM memory capacity -- that enable dramatically higher performance for inference. Dynamo provides a distributed inference orchestration layer that g

## Rebuilding AUTOMATIC1111 with Gradio Workflow

DevFeed: [Rebuilding AUTOMATIC1111 with Gradio Workflow](<https://devfeed.tech/articles/rebuilding-automatic1111-with-gradio-workflow-7233.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/gradio-workflow-1111>)

Author: yuvraj sharma; Abubakar Abid

Published: 2026-09-10T00:00:00Z

Content type: tutorial

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [AI Development](<https://devfeed.tech/topics/ai-development.md>), [Graphs](<https://devfeed.tech/topics/graphs.md>), [stable-diffusion](<https://devfeed.tech/topics/stable-diffusion.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [hugging face](<https://devfeed.tech/topics/hugging-face.md>), [vlm](<https://devfeed.tech/topics/vlm.md>)

Tags: [automatic1111](<https://devfeed.tech/tags/automatic1111.md>), [comfyui](<https://devfeed.tech/tags/comfyui.md>), [diffusion](<https://devfeed.tech/tags/diffusion.md>), [flux](<https://devfeed.tech/tags/flux.md>), [gradio](<https://devfeed.tech/tags/gradio.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [image-to-image](<https://devfeed.tech/tags/image-to-image.md>), [image-to-video](<https://devfeed.tech/tags/image-to-video.md>), [inference](<https://devfeed.tech/tags/inference.md>), [inference-providers](<https://devfeed.tech/tags/inference-providers.md>), [llm](<https://devfeed.tech/tags/llm.md>), [models](<https://devfeed.tech/tags/models.md>), [python](<https://devfeed.tech/tags/python.md>), [space](<https://devfeed.tech/tags/space.md>), [spaces](<https://devfeed.tech/tags/spaces.md>), [text-to-image](<https://devfeed.tech/tags/text-to-image.md>), [vlm](<https://devfeed.tech/tags/vlm.md>), [workflow](<https://devfeed.tech/tags/workflow.md>), [workflows](<https://devfeed.tech/tags/workflows.md>)

### AI overview

A walkthrough of Workflow1111, a Gradio graph that recreates AUTOMATIC1111-style media pipelines with connected operator nodes for image generation, editing, prompting, and related tasks.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## NeoMME: an efficient Multimodal-native and Multilingual Encoder

DevFeed: [NeoMME: an efficient Multimodal-native and Multilingual Encoder](<https://devfeed.tech/articles/neomme-an-efficient-multimodal-native-and-multilingual-encoder-7011.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/Hcompany/neomme>)

Author: Tony Wu; Aurélien Lac

Published: 2026-09-03T13:13:48Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [vlm](<https://devfeed.tech/topics/vlm.md>), [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [GPU](<https://devfeed.tech/topics/gpu.md>)

Tags: [apache](<https://devfeed.tech/tags/apache.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [diffusion](<https://devfeed.tech/tags/diffusion.md>), [embeddings](<https://devfeed.tech/tags/embeddings.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [multimodal](<https://devfeed.tech/tags/multimodal.md>), [quantization](<https://devfeed.tech/tags/quantization.md>), [retrieval](<https://devfeed.tech/tags/retrieval.md>), [training](<https://devfeed.tech/tags/training.md>), [transformers](<https://devfeed.tech/tags/transformers.md>), [vector](<https://devfeed.tech/tags/vector.md>), [vision](<https://devfeed.tech/tags/vision.md>), [vlm](<https://devfeed.tech/tags/vlm.md>)

### AI overview

NeoMME is a family of multilingual multimodal encoders trained from scratch with a masked discrete-diffusion objective. It uses one bidirectional Transformer for text tokens and image patches, and is fine-tuned for visual document retrieval with dense and late-interaction embeddings.

### Source excerpt

We introduce NeoMME, a family of 260M and 800M multilingual multimodal encoders. Unlike many generative visual language models, NeoMME does not use a separate pretrained vision tower or a causal language model. A single bidirectional Transformer processes both text tokens and raw image patches, and we train the entire model from scratch with a masked discrete-diffusion objective. We fine-tuned NeoMME for visual document retrieval using ColPali's page-image approach.

## Training Yandex's Alice Omnimodel to Integrate Text and Images

DevFeed: [Training Yandex's Alice Omnimodel to Integrate Text and Images](<https://devfeed.tech/articles/ai-vlm-llm-24891.md>)

Original publisher: [Read original article](<https://habr.com/ru/companies/yandex/articles/1077274/>)

Author: formica\_rufa (Яндекс)

Published: 2026-09-03T07:03:43Z

Content type: tutorial

Language: ru

Sources: [Яндекс - Как мы делаем Яндекс / Статьи](<https://devfeed.tech/sources/source.md>)

Topics: [Large Language Model](<https://devfeed.tech/topics/llm.md>), [vlm](<https://devfeed.tech/topics/vlm.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Mixture of Experts (MoE)](<https://devfeed.tech/topics/mixture-of-experts-moe.md>), [qwen](<https://devfeed.tech/topics/qwen.md>), [Mercury](<https://devfeed.tech/topics/mercury-lang.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [llm](<https://devfeed.tech/tags/llm.md>), [ml](<https://devfeed.tech/tags/ml.md>), [moe](<https://devfeed.tech/tags/moe.md>), [qwen](<https://devfeed.tech/tags/qwen.md>), [tag-055aee430837](<https://devfeed.tech/tags/tag-055aee430837.md>), [tag-61cd5a476b1d](<https://devfeed.tech/tags/tag-61cd5a476b1d.md>), [tag-831b63de9433](<https://devfeed.tech/tags/tag-831b63de9433.md>), [tag-86b843454893](<https://devfeed.tech/tags/tag-86b843454893.md>), [tag-95a2c958e46b](<https://devfeed.tech/tags/tag-95a2c958e46b.md>), [tag-ef0b1bf200df](<https://devfeed.tech/tags/tag-ef0b1bf200df.md>), [vlm](<https://devfeed.tech/tags/vlm.md>)

### AI overview

Yandex describes its work on an Alice omnimodel that combines a text LLM and a visual VLM into one model for text and image interactions. The article focuses on lessons from training and alignment, including the role of MoE architecture and reinforcement learning.

### Source excerpt

Ещё недавно Алиса отвечала на текст и на картинку будто двумя разными голосами. Под капотом и правда жили две генеративные модели: текстовая LLM и визуальная VLM, а между ними -- стена из непрозрачного роутинга, разных форматов ответов и разной вёрстки. Почти год мы сводили их в одну омнимодель -- такую, которая воспринимает текст и изображения как единое целое, без переключений за кадром. Получилось не всё и не сразу, но путь вышел поучительным, и в этой статье я хочу поделиться тем, что мы поняли про обучение таких моделей. Попутно -- несколько неочевидных поворотов: почему за два года до этого та же затея разваливалась, что изменила MoE-архитектура, почему омнипретрейн пришлось собирать с конца и почему один вид RL переезжает на большую модель легко, а другой рассыпается прямо на глазах. Меня зовут Алексей Григорьев, я представляю большую команду разработки омнимодели Яндекса. Вместе с моим коллегой Данилой Кашиным я расскажу про все технические грабли не со стороны наблюдателя, а как их непосредственный собиратель. Но рассказывать я буду с акцентом не на красивом замысле, а на самой болезненной части -- алайнменте. Читать далее

## PereStruct: Modular Pipeline and Dataset for Parsing Historical Newspapers

DevFeed: [PereStruct: Modular Pipeline and Dataset for Parsing Historical Newspapers](<https://devfeed.tech/articles/vlm-perestruct-24889.md>)

Original publisher: [Read original article](<https://habr.com/ru/companies/yandex/articles/1076770/>)

Author: makSShan (Яндекс, Yandex Cloud & Yandex Infrastructure)

Published: 2026-09-01T07:05:21Z

Content type: tutorial

Language: ru

Sources: [Яндекс - Как мы делаем Яндекс / Статьи](<https://devfeed.tech/sources/source.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [яндекс](<https://devfeed.tech/topics/tag-4004cf5948d3.md>), [vlm](<https://devfeed.tech/topics/vlm.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-studio](<https://devfeed.tech/tags/ai-studio.md>), [bleu](<https://devfeed.tech/tags/bleu.md>), [computervision](<https://devfeed.tech/tags/computervision.md>), [ocr](<https://devfeed.tech/tags/ocr.md>), [perestruct](<https://devfeed.tech/tags/perestruct.md>), [rouge](<https://devfeed.tech/tags/rouge.md>), [tag-4004cf5948d3](<https://devfeed.tech/tags/tag-4004cf5948d3.md>), [tag-65c8d6d9e736](<https://devfeed.tech/tags/tag-65c8d6d9e736.md>), [tag-9bf5e01ce62e](<https://devfeed.tech/tags/tag-9bf5e01ce62e.md>), [tag-d27a0708d400](<https://devfeed.tech/tags/tag-d27a0708d400.md>), [vision](<https://devfeed.tech/tags/vision.md>), [vlm](<https://devfeed.tech/tags/vlm.md>), [yandex-ai-studio](<https://devfeed.tech/tags/yandex-ai-studio.md>), [yolo](<https://devfeed.tech/tags/yolo.md>)

### AI overview

The article presents PereStruct, a modular pipeline for reconstructing articles from historical newspaper scans. It combines YOLO-based layout detection, Yandex Vision OCR, Yandex AI Studio models for error correction, and a semantic model for assembling article blocks; the authors also publish code, an annotated dataset, and a benchmark.

### Source excerpt

Попробуйте открыть скан советской газеты и прочитать одну статью от начала до конца. Человек быстро замечает крупный заголовок, продолжение в соседней колонке и подпись под фотографией. Для алгоритма перед ним -- это выцветшая страница с десятками тесно расположенных прямоугольников, нестандартными шрифтами и неоднозначным порядком чтения. Даже если OCR правильно распознаёт почти все слова, на выходе ещё не получится документ. Нужно понять, какие фрагменты относятся к одной статье, где её начало, в каком порядке соединить блоки и что не следует включать в основной текст. Мы разработали PereStruct -- модульный пайплайн для разбора исторических газет. Он объединяет детектор вёрстки на базе YOLO, Yandex Vision OCR, коррекцию ошибок с помощью моделей Yandex AI Studio и отдельную модель семантической сборки статей. Вместе с кодом мы публикуем размеченный датасет и бенчмарк, чтобы другие команды могли изучать подход и ставить эксперименты на исторических документах. Читать далее

## How Yandex combined image and document text signals for image-search ranking

DevFeed: [How Yandex combined image and document text signals for image-search ranking](<https://devfeed.tech/articles/article-24877.md>)

Original publisher: [Read original article](<https://habr.com/ru/companies/yandex/articles/1066946/>)

Author: nikolaevkona (Яндекс)

Published: 2026-08-10T08:00:19Z

Content type: tutorial

Language: ru

Sources: [Яндекс - Как мы делаем Яндекс / Статьи](<https://devfeed.tech/sources/source.md>)

Topics: [яндекс](<https://devfeed.tech/topics/tag-4004cf5948d3.md>), [vlm](<https://devfeed.tech/topics/vlm.md>), [Image](<https://devfeed.tech/topics/image.md>), [realtime](<https://devfeed.tech/topics/realtime.md>)

Tags: [image](<https://devfeed.tech/tags/image.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [ml](<https://devfeed.tech/tags/ml.md>), [realtime](<https://devfeed.tech/tags/realtime.md>), [tag-4004cf5948d3](<https://devfeed.tech/tags/tag-4004cf5948d3.md>), [tag-8be2dbf54d97](<https://devfeed.tech/tags/tag-8be2dbf54d97.md>), [vlm](<https://devfeed.tech/tags/vlm.md>), [yandex-e983188bc433](<https://devfeed.tech/tags/yandex-e983188bc433.md>)

### AI overview

Yandex describes using multimodal vision-language models to jointly assess an image and its associated document text for image-search ranking. The team distilled a larger model into lighter models for different pipeline stages and reports deployment in real-time search.

### Source excerpt

Исторически в Яндекс Картинках релевантность документа оценивалась по двум сигналам: насколько запросу подходит само изображение и насколько -- текст, связанный с этим изображением. Такой подход позволяет учесть контент картинки и не провалиться на визуально трудноотличимых объектах, однако он же порождает проблему: в "серой зоне", когда текстовая релевантность не сонаправлена с картиночной, становится неочевидно, как именно агрегировать сигналы в финальный скор релевантности. Привет! Я Константин Николаев, занимаюсь внедрением нейротехнологий в Поиске по картинкам. В этой статье я расскажу, как наша команда научила модели смотреть на картинку и читать текст документа одновременно: начали с тяжёлой мультимодальной VLM ради максимального качества, а затем дистиллировали её в набор лёгких моделей -- по одной под каждую стадию пайплайна. Что из этого удалось довести до realtime-поиска с десятками тысяч запросов в секунду и как совместный анализ двух модальностей добавил 5% релевантных картинок в топ выдачи -- под катом. Читать далее

## Meta is back with Muse Glimmer: local, agentic, multimodal, and open source

DevFeed: [Meta is back with Muse Glimmer: local, agentic, multimodal, and open source](<https://devfeed.tech/articles/meta-is-back-with-muse-glimmer-local-agentic-multimodal-and-open-source-7362.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/muse-glimmer>)

Author: Pedro Cuenca; merve; ben burtenshaw; Aritra Roy Gosthipaty

Published: 2026-08-10T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [vlm](<https://devfeed.tech/topics/vlm.md>), [multimodal](<https://devfeed.tech/topics/multimodal.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [hugging face](<https://devfeed.tech/topics/hugging-face.md>), [Benchmark](<https://devfeed.tech/topics/benchmark.md>), [coding](<https://devfeed.tech/topics/coding.md>)

Tags: [agentic](<https://devfeed.tech/tags/agentic.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [hub](<https://devfeed.tech/tags/hub.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [images](<https://devfeed.tech/tags/images.md>), [llama-cpp](<https://devfeed.tech/tags/llama-cpp.md>), [llms](<https://devfeed.tech/tags/llms.md>), [local](<https://devfeed.tech/tags/local.md>), [meta](<https://devfeed.tech/tags/meta.md>), [model](<https://devfeed.tech/tags/model.md>), [multimodal](<https://devfeed.tech/tags/multimodal.md>), [muse](<https://devfeed.tech/tags/muse.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [videos](<https://devfeed.tech/tags/videos.md>), [vllm](<https://devfeed.tech/tags/vllm.md>), [vlm](<https://devfeed.tech/tags/vlm.md>), [vlms](<https://devfeed.tech/tags/vlms.md>)

### AI overview

Hugging Face presents Muse Glimmer, a local, agentic, multimodal, open-source 30B-parameter vision-language model developed with Meta. The article outlines its vision and language architecture, benchmark context, optional speculative decoding for faster generation, and support for both images and videos.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## Beyond VLAs: How World Action Models Reshape Robot Manipulation

DevFeed: [Beyond VLAs: How World Action Models Reshape Robot Manipulation](<https://devfeed.tech/articles/beyond-vlas-how-world-action-models-reshape-robot-manipulation-6764.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/beyond-vlas-how-world-action-models-reshape-robot-manipulation/>)

Author: Michelle Horton

Published: 2026-08-04T16:00:00Z

Content type: article

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [Robotics](<https://devfeed.tech/topics/robotics.md>), [World models](<https://devfeed.tech/topics/world-models.md>), [vlm](<https://devfeed.tech/topics/vlm.md>), [post-training](<https://devfeed.tech/topics/post-training.md>), [Cosmos](<https://devfeed.tech/topics/cosmos.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [NVIDIA Research](<https://devfeed.tech/topics/nvidia-research.md>)

Tags: [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [cosmos](<https://devfeed.tech/tags/cosmos.md>), [featured](<https://devfeed.tech/tags/featured.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [nvidia-research](<https://devfeed.tech/tags/nvidia-research.md>), [physical-ai](<https://devfeed.tech/tags/physical-ai.md>), [post-training](<https://devfeed.tech/tags/post-training.md>), [robot-manipulation](<https://devfeed.tech/tags/robot-manipulation.md>), [robotics](<https://devfeed.tech/tags/robotics.md>), [simulation-modeling-design](<https://devfeed.tech/tags/simulation-modeling-design.md>), [thor](<https://devfeed.tech/tags/thor.md>), [vlm](<https://devfeed.tech/tags/vlm.md>), [world-model](<https://devfeed.tech/tags/world-model.md>), [zero-shot](<https://devfeed.tech/tags/zero-shot.md>)

### AI overview

The article explains how World Action Models (WAMs) use video world models as backbones for robot policies, addressing the physical-generalization limitations of vision-language-action models. It discusses post-training WAMs into specialized policies and presents NVIDIA Cosmos 3 as a foundation for building them.

### Source excerpt

A central challenge in robotics is building policies that generalize beyond the demonstrations they're trained on. A policy that succeeds in a training scene...

## NVIDIA Ising Enables Fully Automated Quantum Computer Calibration with Enhanced In-Context Learning

DevFeed: [NVIDIA Ising Enables Fully Automated Quantum Computer Calibration with Enhanced In-Context Learning](<https://devfeed.tech/articles/nvidia-ising-enables-fully-automated-quantum-computer-calibration-with-enhanced-in-context-learning-6895.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/nvidia-ising-enables-fully-automated-quantum-computer-calibration-with-enhanced-in-context-learning/>)

Author: Tanya Lenz

Published: 2026-07-27T16:00:00Z

Content type: article

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [Ising](<https://devfeed.tech/topics/ising.md>), [Quantum Computing](<https://devfeed.tech/topics/quantum-computing.md>), [vlm](<https://devfeed.tech/topics/vlm.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [Benchmark](<https://devfeed.tech/topics/benchmark.md>), [benchmarking](<https://devfeed.tech/topics/benchmarking.md>), [NVFP4](<https://devfeed.tech/topics/nvfp4.md>), [DGX Spark](<https://devfeed.tech/topics/dgx-spark.md>)

Tags: [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [benchmarking](<https://devfeed.tech/tags/benchmarking.md>), [data-science](<https://devfeed.tech/tags/data-science.md>), [dgx-spark](<https://devfeed.tech/tags/dgx-spark.md>), [featured](<https://devfeed.tech/tags/featured.md>), [ising](<https://devfeed.tech/tags/ising.md>), [nvfp4](<https://devfeed.tech/tags/nvfp4.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [quantum](<https://devfeed.tech/tags/quantum.md>), [quantum-computing](<https://devfeed.tech/tags/quantum-computing.md>), [simulation-modeling-design](<https://devfeed.tech/tags/simulation-modeling-design.md>), [training-ai-models](<https://devfeed.tech/tags/training-ai-models.md>), [vlm](<https://devfeed.tech/tags/vlm.md>), [vlms](<https://devfeed.tech/tags/vlms.md>)

### AI overview

NVIDIA Ising Calibration 1.5 is an open-source vision-language model for interpreting quantum-processor diagnostics and recommending calibration actions. The article highlights zero-shot and in-context learning evaluation on QCalEval, plus an NVFP4-quantized version for local deployment.

### Source excerpt

NVIDIA Ising Calibration is an open source vision language model (VLM) designed to interpret diagnostic outputs from quantum processors and determine how they...

## LeRobot v0.6.0: Imagine, Evaluate, Improve

DevFeed: [LeRobot v0.6.0: Imagine, Evaluate, Improve](<https://devfeed.tech/articles/lerobot-v0-6-0-imagine-evaluate-improve-7329.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/lerobot-release-v060>)

Author: Steven Palma; Pepijn Kooijmans; Caroline Pascal; Khalil Meftah; Maxime Ellerbach; Martino Russi; Nikodem Bartnik; Nicolas Rabault; Thomas Wolf

Published: 2026-07-07T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [lerobot](<https://devfeed.tech/topics/lerobot.md>), [World models](<https://devfeed.tech/topics/world-models.md>), [Robotics](<https://devfeed.tech/topics/robotics.md>), [vlm](<https://devfeed.tech/topics/vlm.md>), [Cosmos](<https://devfeed.tech/topics/cosmos.md>), [foundation-models](<https://devfeed.tech/topics/foundation-models.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>)

Tags: [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [cli](<https://devfeed.tech/tags/cli.md>), [cosmos](<https://devfeed.tech/tags/cosmos.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [lerobot](<https://devfeed.tech/tags/lerobot.md>), [models](<https://devfeed.tech/tags/models.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [robotics](<https://devfeed.tech/tags/robotics.md>), [science](<https://devfeed.tech/tags/science.md>), [training](<https://devfeed.tech/tags/training.md>), [vlm](<https://devfeed.tech/tags/vlm.md>), [world-model](<https://devfeed.tech/tags/world-model.md>)

### AI overview

LeRobot v0.6.0 adds world-model policies, new vision-language-action models, reward-model APIs, simulation benchmarks, human-in-the-loop CLI corrections, FSDP and cloud training. It also expands dataset capabilities with depth support, automated language annotation, custom video encoding, and faster data loading.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## PRX Part 4: Our Data Strategy

DevFeed: [PRX Part 4: Our Data Strategy](<https://devfeed.tech/articles/prx-part-4-our-data-strategy-7039.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/Photoroom/prx-part4-data>)

Author: Roman Frigg; David Bertoin; Jon Almazán

Published: 2026-07-06T15:30:55Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [Training AI Models](<https://devfeed.tech/topics/training-ai-models.md>), [vlm](<https://devfeed.tech/topics/vlm.md>)

Tags: [data](<https://devfeed.tech/tags/data.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [diversity](<https://devfeed.tech/tags/diversity.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [images](<https://devfeed.tech/tags/images.md>), [tooling](<https://devfeed.tech/tags/tooling.md>), [training](<https://devfeed.tech/tags/training.md>), [vlm](<https://devfeed.tech/tags/vlm.md>)

### AI overview

The article describes assembling a diverse pre-training corpus for PRX from public and internal datasets, recaptioning images with a vision-language model, and prioritizing broad, faithful long-caption coverage over aesthetic filtering.

### Source excerpt

In one sentence: we assemble training data from a mix of public and internal datasets, re-caption the images with a VLM, and turn the result into the streamable corpus we trained PRX on. At a high level, the data pipeline looks like this: In the following we will dive into it in detail. The goal was to assemble a large, diverse dataset for pre-training.

## Supercharge your OCR Pipelines with Open Models

DevFeed: [Supercharge your OCR Pipelines with Open Models](<https://devfeed.tech/articles/supercharge-your-ocr-pipelines-with-open-models-7406.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/ocr-open-models>)

Author: merve; Aritra Roy Gosthipaty; Daniel van Strien; Hynek Kydlicek; Andres Marafioti; Vaibhav Srivastav; Pedro Cuenca

Published: 2025-10-21T00:00:00Z

Content type: tutorial

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [document ai](<https://devfeed.tech/topics/document-ai.md>), [Computer vision](<https://devfeed.tech/topics/computer-vision.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [multimodal](<https://devfeed.tech/topics/multimodal.md>), [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [Language models](<https://devfeed.tech/topics/language-models.md>)

Tags: [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [computer-vision](<https://devfeed.tech/tags/computer-vision.md>), [document-ai](<https://devfeed.tech/tags/document-ai.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [html](<https://devfeed.tech/tags/html.md>), [llm](<https://devfeed.tech/tags/llm.md>), [markdown](<https://devfeed.tech/tags/markdown.md>), [models](<https://devfeed.tech/tags/models.md>), [multimodal](<https://devfeed.tech/tags/multimodal.md>), [ocr](<https://devfeed.tech/tags/ocr.md>), [open](<https://devfeed.tech/tags/open.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [pipelines](<https://devfeed.tech/tags/pipelines.md>), [qa](<https://devfeed.tech/tags/qa.md>), [retrieval](<https://devfeed.tech/tags/retrieval.md>), [vision](<https://devfeed.tech/tags/vision.md>), [vlm](<https://devfeed.tech/tags/vlm.md>)

### AI overview

This guide surveys open-weight OCR and vision-language models for document AI. It explains their capabilities, output formats, multimodal document retrieval, document question answering, and the tradeoffs between fine-tuning and using models out of the box.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## Get your VLM running in 3 simple steps on Intel CPUs

DevFeed: [Get your VLM running in 3 simple steps on Intel CPUs](<https://devfeed.tech/articles/get-your-vlm-running-in-3-simple-steps-on-intel-cpus-7431.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/openvino-vlm>)

Author: Ezequiel Lanza; Helena; Nikita; Ella Charlaix; Ilyas Moutawwakil

Published: 2025-10-15T00:00:00Z

Content type: tutorial

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [vlm](<https://devfeed.tech/topics/vlm.md>), [smolvlm](<https://devfeed.tech/topics/smolvlm.md>), [optimum](<https://devfeed.tech/topics/optimum.md>), [quantization](<https://devfeed.tech/topics/quantization.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [intel](<https://devfeed.tech/topics/intel.md>)

Tags: [inference](<https://devfeed.tech/tags/inference.md>), [intel](<https://devfeed.tech/tags/intel.md>), [optimum](<https://devfeed.tech/tags/optimum.md>), [quantization](<https://devfeed.tech/tags/quantization.md>), [smolvlm](<https://devfeed.tech/tags/smolvlm.md>), [vlm](<https://devfeed.tech/tags/vlm.md>)

### AI overview

A tutorial explains how to run SmolVLM locally with Optimum Intel and OpenVINO, then optimize it for lower memory use and faster inference through quantization.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## Smol2Operator: Post-Training GUI Agents for Computer Use

DevFeed: [Smol2Operator: Post-Training GUI Agents for Computer Use](<https://devfeed.tech/articles/smol2operator-post-training-gui-agents-for-computer-use-7476.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/smol2operator>)

Author: Amir Mahla; merve; Sergio Paniego; Vaibhav Srivastav; Lewis Tunstall

Published: 2025-09-23T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [gui-agents](<https://devfeed.tech/topics/gui-agents.md>), [computer-use](<https://devfeed.tech/topics/computer-use.md>), [post-training](<https://devfeed.tech/topics/post-training.md>), [vlm](<https://devfeed.tech/topics/vlm.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [Benchmark](<https://devfeed.tech/topics/benchmark.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>)

Tags: [agentic](<https://devfeed.tech/tags/agentic.md>), [agents](<https://devfeed.tech/tags/agents.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [blog](<https://devfeed.tech/tags/blog.md>), [community](<https://devfeed.tech/tags/community.md>), [computer-use](<https://devfeed.tech/tags/computer-use.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [gui](<https://devfeed.tech/tags/gui.md>), [gui-agents](<https://devfeed.tech/tags/gui-agents.md>), [language-models](<https://devfeed.tech/tags/language-models.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [post-training](<https://devfeed.tech/tags/post-training.md>), [science](<https://devfeed.tech/tags/science.md>), [training](<https://devfeed.tech/tags/training.md>), [vision](<https://devfeed.tech/tags/vision.md>), [vlm](<https://devfeed.tech/tags/vlm.md>)

### AI overview

This article presents a multi-phase method for post-training vision-language models to perform GUI automation and computer-use tasks. It covers data processing, action standardization, grounding, supervised fine-tuning, agentic reasoning, and evaluation with the ScreenSpot-v2 benchmark, using SmolVLM2-2.2B-Instruct as the baseline model.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## Vision Language Model Alignment in TRL ⚡

DevFeed: [Vision Language Model Alignment in TRL ⚡](<https://devfeed.tech/articles/vision-language-model-alignment-in-trl-7542.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/trl-vlm-alignment>)

Author: Sergio Paniego; merve; Quentin Gallouédec; Kashif Rasul; Aritra Roy Gosthipaty

Published: 2025-08-07T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [trl](<https://devfeed.tech/topics/trl.md>), [vlm](<https://devfeed.tech/topics/vlm.md>), [multimodal](<https://devfeed.tech/topics/multimodal.md>), [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>)

Tags: [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [dpo](<https://devfeed.tech/tags/dpo.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [grpo](<https://devfeed.tech/tags/grpo.md>), [model](<https://devfeed.tech/tags/model.md>), [multimodal](<https://devfeed.tech/tags/multimodal.md>), [trl](<https://devfeed.tech/tags/trl.md>), [vision](<https://devfeed.tech/tags/vision.md>), [vlm](<https://devfeed.tech/tags/vlm.md>)

### AI overview

This article presents new and extended methods in TRL for aligning Vision Language Models with human preferences. It covers Mixed Preference Optimization, Group Relative Policy Optimization, Group Sequence Policy Optimization, Reinforce Leave One Out, and Online Direct Preference Optimization, along with native supervised fine-tuning support, training scripts, and demo notebooks.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## ScreenEnv: Deploy your full stack Desktop Agent

DevFeed: [ScreenEnv: Deploy your full stack Desktop Agent](<https://devfeed.tech/articles/screenenv-deploy-your-full-stack-desktop-agent-7467.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/screenenv>)

Author: Amir Mahla; Aymeric Roucher

Published: 2025-07-10T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [AI Agent](<https://devfeed.tech/topics/ai-agent.md>), [Docker Container](<https://devfeed.tech/topics/docker-container.md>), [Model Context Protocol](<https://devfeed.tech/topics/model-context-protocol.md>), [computer-use](<https://devfeed.tech/topics/computer-use.md>), [vlm](<https://devfeed.tech/topics/vlm.md>), [GUI](<https://devfeed.tech/topics/gui.md>), [cross-platform](<https://devfeed.tech/topics/cross-platform.md>), [GitHub](<https://devfeed.tech/topics/github.md>), [Linux](<https://devfeed.tech/topics/linux.md>), [Windows](<https://devfeed.tech/topics/windows.md>), [Android](<https://devfeed.tech/topics/android.md>), [macOS](<https://devfeed.tech/topics/macos.md>)

Tags: [agent](<https://devfeed.tech/tags/agent.md>), [agents](<https://devfeed.tech/tags/agents.md>), [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [community](<https://devfeed.tech/tags/community.md>), [cross-platform](<https://devfeed.tech/tags/cross-platform.md>), [docker](<https://devfeed.tech/tags/docker.md>), [docker-container](<https://devfeed.tech/tags/docker-container.md>), [github](<https://devfeed.tech/tags/github.md>), [gui](<https://devfeed.tech/tags/gui.md>), [mcp](<https://devfeed.tech/tags/mcp.md>), [model-context-protocol](<https://devfeed.tech/tags/model-context-protocol.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [vlm](<https://devfeed.tech/tags/vlm.md>)

### AI overview

ScreenEnv is a Docker-based sandboxed desktop environment for automating GUI applications and building AI agents that can control a complete desktop session. It supports mouse and keyboard input, window and file management, application launching, terminal commands, and session recording, with integrations through both the Model Context Protocol and a direct Sandbox API. The article also describes building custom desktop agents and plans for Android, macOS, and Windows support.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## Efficient MultiModal Data Pipeline

DevFeed: [Efficient MultiModal Data Pipeline](<https://devfeed.tech/articles/efficient-multimodal-data-pipeline-7355.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/mmdp>)

Author: Aritra Roy Gosthipaty; Luis; Andres Marafioti; Sergio Paniego; Pedro Cuenca

Published: 2025-07-08T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [multimodal](<https://devfeed.tech/topics/multimodal.md>), [data](<https://devfeed.tech/topics/data.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [Mathematics](<https://devfeed.tech/topics/mathematics.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>)

Tags: [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [batching](<https://devfeed.tech/tags/batching.md>), [community](<https://devfeed.tech/tags/community.md>), [data](<https://devfeed.tech/tags/data.md>), [data-pipeline](<https://devfeed.tech/tags/data-pipeline.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [multimodal](<https://devfeed.tech/tags/multimodal.md>), [nanovlm](<https://devfeed.tech/tags/nanovlm.md>), [open](<https://devfeed.tech/tags/open.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [pipeline](<https://devfeed.tech/tags/pipeline.md>), [research](<https://devfeed.tech/tags/research.md>), [vlm](<https://devfeed.tech/tags/vlm.md>)

### AI overview

This article explains how to build an efficient multimodal data pipeline for nanoVLM training. It examines waste caused by idle GPUs and excessive padding, then introduces progressively improved data preparation and batching strategies, including a knapsack-based approach to fit more useful data into each batch.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## Welcome the NVIDIA Llama Nemotron Nano VLM to Hugging Face Hub

DevFeed: [Welcome the NVIDIA Llama Nemotron Nano VLM to Hugging Face Hub](<https://devfeed.tech/articles/welcome-the-nvidia-llama-nemotron-nano-vlm-to-hugging-face-hub-7384.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/nvidia/llama-nemotron-nano-vl>)

Author: Amanda Saunders; Amala Sanjay Deshmukh; Kateryna Chumachenko; Annie Surla; Karan; Tuomas Rintamaki; Matthieu Le; Yu Yao; Chen Cui; Timo Roman; Zhiding Yu; Mike Ranzinger

Published: 2025-06-27T21:09:27Z

Content type: release

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [vlm](<https://devfeed.tech/topics/vlm.md>), [llama](<https://devfeed.tech/topics/llama.md>), [Nemotron](<https://devfeed.tech/topics/nemotron.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [hugging face](<https://devfeed.tech/topics/hugging-face.md>), [multimodal](<https://devfeed.tech/topics/multimodal.md>), [Benchmark](<https://devfeed.tech/topics/benchmark.md>), [NeMo](<https://devfeed.tech/topics/nemo.md>), [datasets](<https://devfeed.tech/topics/datasets.md>)

Tags: [benchmark](<https://devfeed.tech/tags/benchmark.md>), [data](<https://devfeed.tech/tags/data.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [idp](<https://devfeed.tech/tags/idp.md>), [llama](<https://devfeed.tech/tags/llama.md>), [multimodal](<https://devfeed.tech/tags/multimodal.md>), [nemo](<https://devfeed.tech/tags/nemo.md>), [nemotron](<https://devfeed.tech/tags/nemotron.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [ocr](<https://devfeed.tech/tags/ocr.md>), [performance](<https://devfeed.tech/tags/performance.md>), [recognition](<https://devfeed.tech/tags/recognition.md>), [train](<https://devfeed.tech/tags/train.md>), [use-cases](<https://devfeed.tech/tags/use-cases.md>), [vision](<https://devfeed.tech/tags/vision.md>), [vlm](<https://devfeed.tech/tags/vlm.md>), [workflows](<https://devfeed.tech/tags/workflows.md>)

### AI overview

NVIDIA Llama Nemotron Nano VL is an 8B vision-language model for intelligent document processing. Available on Hugging Face, it extracts and interprets text, tables, charts, diagrams, and other information from complex documents.

### Source excerpt

NVIDIA Llama Nemotron Nano VL is a state-of-the-art 8B Vision Language Model (VLM) designed for intelligent document processing, offering high accuracy and multimodal understanding. Available on Hugging Face, it excels in extracting and understanding information from complex documents like invoices, receipts, contracts, and more.

## Gemma 3n fully available in the open-source ecosystem!

DevFeed: [Gemma 3n fully available in the open-source ecosystem!](<https://devfeed.tech/articles/gemma-3n-fully-available-in-the-open-source-ecosystem-7213.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/gemma3n>)

Author: Aritra Roy Gosthipaty; Pedro Cuenca; Sergio Paniego; Vaibhav Srivastav; Christopher Fleetwood; Joshua; Steven Zheng; Kashif Rasul

Published: 2025-06-26T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [gemma](<https://devfeed.tech/topics/gemma.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [AI Development](<https://devfeed.tech/topics/ai-development.md>), [llama.cpp](<https://devfeed.tech/topics/llama-cpp.md>), [MLX](<https://devfeed.tech/topics/mlx.md>), [Ollama](<https://devfeed.tech/topics/ollama.md>), [transformers.js](<https://devfeed.tech/topics/transformers-js.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [Hardware](<https://devfeed.tech/topics/hardware.md>), [timm](<https://devfeed.tech/topics/timm.md>), [asr](<https://devfeed.tech/topics/asr.md>), [Embeddings](<https://devfeed.tech/topics/embeddings.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [audio](<https://devfeed.tech/tags/audio.md>), [community](<https://devfeed.tech/tags/community.md>), [embeddings](<https://devfeed.tech/tags/embeddings.md>), [gemma](<https://devfeed.tech/tags/gemma.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [implementation](<https://devfeed.tech/tags/implementation.md>), [llama-cpp](<https://devfeed.tech/tags/llama-cpp.md>), [llm](<https://devfeed.tech/tags/llm.md>), [mlx](<https://devfeed.tech/tags/mlx.md>), [multimodal](<https://devfeed.tech/tags/multimodal.md>), [ollama](<https://devfeed.tech/tags/ollama.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [release](<https://devfeed.tech/tags/release.md>), [research](<https://devfeed.tech/tags/research.md>), [speech](<https://devfeed.tech/tags/speech.md>), [timm](<https://devfeed.tech/tags/timm.md>), [transformers](<https://devfeed.tech/tags/transformers.md>), [transformers-js](<https://devfeed.tech/tags/transformers-js.md>), [translation](<https://devfeed.tech/tags/translation.md>), [vision](<https://devfeed.tech/tags/vision.md>), [vlm](<https://devfeed.tech/tags/vlm.md>)

### AI overview

This article announces the availability of Gemma 3n in major open-source libraries and presents practical usage and fine-tuning examples. It describes the model variants, memory-efficient hardware requirements, multimodal audio and vision encoders, speech-to-text and translation capabilities, and architectural features including MatFormer and Per-Layer Embeddings.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## ScreenSuite - The most comprehensive evaluation suite for GUI Agents!

DevFeed: [ScreenSuite - The most comprehensive evaluation suite for GUI Agents!](<https://devfeed.tech/articles/screensuite-the-most-comprehensive-evaluation-suite-for-gui-agents-7468.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/screensuite>)

Author: Amir Mahla; Aymeric Roucher; Thomas Wolf

Published: 2025-06-06T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [screensuite](<https://devfeed.tech/topics/screensuite.md>), [gui-agents](<https://devfeed.tech/topics/gui-agents.md>), [computer-use](<https://devfeed.tech/topics/computer-use.md>), [Benchmark](<https://devfeed.tech/topics/benchmark.md>), [benchmarking](<https://devfeed.tech/topics/benchmarking.md>), [AI Chat](<https://devfeed.tech/topics/ai-chat.md>), [Claude](<https://devfeed.tech/topics/claude.md>)

Tags: [agents](<https://devfeed.tech/tags/agents.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [benchmarking](<https://devfeed.tech/tags/benchmarking.md>), [computer-use](<https://devfeed.tech/tags/computer-use.md>), [docker](<https://devfeed.tech/tags/docker.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [gui](<https://devfeed.tech/tags/gui.md>), [gui-agents](<https://devfeed.tech/tags/gui-agents.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [screensuite](<https://devfeed.tech/tags/screensuite.md>), [smolagents](<https://devfeed.tech/tags/smolagents.md>), [virtual-machines](<https://devfeed.tech/tags/virtual-machines.md>), [vlm](<https://devfeed.tech/tags/vlm.md>), [vlms](<https://devfeed.tech/tags/vlms.md>)

### AI overview

ScreenSuite is an open benchmarking and evaluation suite for GUI agents. It unifies 13 benchmarks covering perception, grounding, single-step actions, and multi-step agentic capabilities for Vision Language Models, with support for remote desktop sandboxes and Ubuntu or Android virtual machines launched in Docker.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## KV Cache from scratch in nanoVLM

DevFeed: [KV Cache from scratch in nanoVLM](<https://devfeed.tech/articles/kv-cache-from-scratch-in-nanovlm-7304.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/kv-cache>)

Author: Aritra Roy Gosthipaty; Kashif Rasul; Luis; Andres Marafioti; Pedro Cuenca

Published: 2025-06-04T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [Caching](<https://devfeed.tech/topics/caching.md>), [Language models](<https://devfeed.tech/topics/language-models.md>), [Transformer](<https://devfeed.tech/topics/transformer.md>), [PyTorch](<https://devfeed.tech/topics/pytorch.md>)

Tags: [cache](<https://devfeed.tech/tags/cache.md>), [generate](<https://devfeed.tech/tags/generate.md>), [inference](<https://devfeed.tech/tags/inference.md>), [nanovlm](<https://devfeed.tech/tags/nanovlm.md>), [nlp](<https://devfeed.tech/tags/nlp.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [pytorch](<https://devfeed.tech/tags/pytorch.md>), [transformers](<https://devfeed.tech/tags/transformers.md>), [vision](<https://devfeed.tech/tags/vision.md>), [vlm](<https://devfeed.tech/tags/vlm.md>)

### AI overview

The article explains how KV Caching was implemented from scratch in the nanoVLM repository using pure PyTorch, reporting a 38% generation speedup. It describes the sequential nature of autoregressive language-model generation, the repeated computation in transformer self-attention, and how caching previously computed key and value tensors reduces this redundancy.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## nanoVLM: The simplest repository to train your VLM in pure PyTorch

DevFeed: [nanoVLM: The simplest repository to train your VLM in pure PyTorch](<https://devfeed.tech/articles/nanovlm-the-simplest-repository-to-train-your-vlm-in-pure-pytorch-7363.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/nanovlm>)

Author: Aritra Roy Gosthipaty; Luis; Andres Marafioti; Sergio Paniego; merve; Pedro Cuenca; Vaibhav Srivastav

Published: 2025-05-21T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [vlm](<https://devfeed.tech/topics/vlm.md>), [PyTorch](<https://devfeed.tech/topics/pytorch.md>), [Language models](<https://devfeed.tech/topics/language-models.md>), [llama](<https://devfeed.tech/topics/llama.md>), [Transformer](<https://devfeed.tech/topics/transformer.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>)

Tags: [community](<https://devfeed.tech/tags/community.md>), [llama](<https://devfeed.tech/tags/llama.md>), [llm](<https://devfeed.tech/tags/llm.md>), [nanovlm](<https://devfeed.tech/tags/nanovlm.md>), [object-detection](<https://devfeed.tech/tags/object-detection.md>), [open](<https://devfeed.tech/tags/open.md>), [pytorch](<https://devfeed.tech/tags/pytorch.md>), [research](<https://devfeed.tech/tags/research.md>), [train](<https://devfeed.tech/tags/train.md>), [training](<https://devfeed.tech/tags/training.md>), [vision](<https://devfeed.tech/tags/vision.md>), [vlm](<https://devfeed.tech/tags/vlm.md>)

### AI overview

nanoVLM is a minimal, readable pure-PyTorch toolkit for building and training vision-language models. It processes images and text to generate text, focuses on visual question answering, and is designed to help beginners explore VLMs through a compact codebase and guided training workflow.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## Vision Language Models (Better, faster, stronger)

DevFeed: [Vision Language Models (Better, faster, stronger)](<https://devfeed.tech/articles/vision-language-models-better-faster-stronger-7561.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/vlms-2025>)

Author: merve; Sergio Paniego; Aritra Roy Gosthipaty; Pedro Cuenca; Andres Marafioti

Published: 2025-05-12T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [vlm](<https://devfeed.tech/topics/vlm.md>), [multimodal](<https://devfeed.tech/topics/multimodal.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [qwen](<https://devfeed.tech/topics/qwen.md>), [Meta](<https://devfeed.tech/topics/meta.md>), [deepseek](<https://devfeed.tech/topics/deepseek.md>)

Tags: [architecture](<https://devfeed.tech/tags/architecture.md>), [community](<https://devfeed.tech/tags/community.md>), [deepseek](<https://devfeed.tech/tags/deepseek.md>), [embeddings](<https://devfeed.tech/tags/embeddings.md>), [multimodal](<https://devfeed.tech/tags/multimodal.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [qwen](<https://devfeed.tech/tags/qwen.md>), [research](<https://devfeed.tech/tags/research.md>), [streaming](<https://devfeed.tech/tags/streaming.md>), [vision](<https://devfeed.tech/tags/vision.md>), [vlm](<https://devfeed.tech/tags/vlm.md>), [vlms](<https://devfeed.tech/tags/vlms.md>)

### AI overview

This article reviews major developments in vision-language models over the past year, including smaller and more capable models, new architectures, reasoning, agency, long-video understanding, multimodal Retrieval Augmented Generation, and multimodal agents. It also explains any-to-any models and discusses examples including Chameleon, Lumina-mGPT, Qwen 2.5 Omni, MiniCPM-o 2.6, and Janus-Pro-7B.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## Welcoming Llama Guard 4 on Hugging Face Hub

DevFeed: [Welcoming Llama Guard 4 on Hugging Face Hub](<https://devfeed.tech/articles/welcoming-llama-guard-4-on-hugging-face-hub-7333.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/llama-guard-4>)

Author: merve; Aritra Roy Gosthipaty; Sergio Paniego; Pedro Cuenca

Published: 2025-04-29T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [llama](<https://devfeed.tech/topics/llama.md>), [multimodal](<https://devfeed.tech/topics/multimodal.md>), [hugging face](<https://devfeed.tech/topics/hugging-face.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [large-language-models](<https://devfeed.tech/topics/large-language-models.md>), [moe](<https://devfeed.tech/topics/moe.md>), [post-training](<https://devfeed.tech/topics/post-training.md>), [Open Source Models & Datasets](<https://devfeed.tech/topics/open-source-models-datasets.md>)

Tags: [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [language-models](<https://devfeed.tech/tags/language-models.md>), [large-language-models](<https://devfeed.tech/tags/large-language-models.md>), [llama](<https://devfeed.tech/tags/llama.md>), [llm](<https://devfeed.tech/tags/llm.md>), [mixture-of-experts-moe](<https://devfeed.tech/tags/mixture-of-experts-moe.md>), [multimodal](<https://devfeed.tech/tags/multimodal.md>), [post-training](<https://devfeed.tech/tags/post-training.md>), [safety](<https://devfeed.tech/tags/safety.md>), [vision](<https://devfeed.tech/tags/vision.md>), [vlm](<https://devfeed.tech/tags/vlm.md>)

### AI overview

Hugging Face introduces Llama Guard 4, a multimodal safety model for detecting unsafe image and text inputs and outputs. The dense 12B model can run on a single 24 GB GPU, supports multilingual content, and classifies hazards for configurable moderation pipelines.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

[Next page](<https://devfeed.tech/tags/vlm.md?cursor=WyIyMDI1LTA0LTI5VDAwOjAwOjAwKzAwOjAwIiwgImEzYjA3NjkzLTZmZjItNGM3YS1hOWE5LTNhNzBhZjU2ZWJkMCJd>)