# open-source-collab Published articles for open-source-collab. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Run AI workloads on any cloud, store on Hugging Face: zero-egress storage with SkyPilot DevFeed: [Run AI workloads on any cloud, store on Hugging Face: zero-egress storage with SkyPilot]() Original publisher: [Read original article]() Author: Nikhil Jha; Zhanghao Wu; Hope Wang; Adrien Carreira; Julien Chaumond Published: 2026-07-07T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Artificial Intelligence](), [hugging face](), [Cloud](), [datasets](), [GPU](), [Inference](), [model-deployment](), [Containers](), [Open Source]() Tags: [ai](), [artificial-intelligence](), [cloud](), [containers](), [datasets](), [gpu](), [guide](), [hugging-face](), [inference](), [kubernetes](), [open-source](), [open-source-collab](), [partnerships](), [s3](), [skypilot](), [storage]() ### AI overview Hugging Face Storage integrates with SkyPilot so models, datasets, and Spaces can be mounted into jobs running on GPUs across clouds, Kubernetes, Slurm, and on-premises infrastructure. The integration offers zero-egress reads, Xet-backed deduplication, filesystem-level fetching, and local caching across the AI workload lifecycle. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Hugging Face and Cerebras bring Gemma 4 to real-time voice AI DevFeed: [Hugging Face and Cerebras bring Gemma 4 to real-time voice AI]() Original publisher: [Read original article]() Author: Amir Mahla; Andres Marafioti; Leandro von Werra; Saurabh Vyas Published: 2026-07-01T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [voice ai](), [gemma4](), [hugging face](), [Artificial Intelligence](), [Open Source](), [real-time](), [Low Latency](), [Inference](), [Conversational AI](), [Language models](), [systems]() Tags: [ai](), [audio](), [gemma](), [hugging-face](), [inference](), [inference-providers](), [latency](), [low-latency](), [model](), [models](), [multimodal](), [open-source](), [open-source-collab](), [partnerships](), [performance](), [real-time](), [systems](), [text-to-speech](), [voice](), [voice-ai]() ### AI overview Hugging Face and Cerebras present a real-time speech-to-speech pipeline that combines Cerebras inference, Google DeepMind's Gemma 4 31B language model, and Qwen text-to-speech. The modular, open architecture is designed for low latency, predictable performance, and adaptable voice experiences across assistants, robots, products, and research projects. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Using local Gemma and Qwen models to triage OpenClaw issues and pull requests DevFeed: [Using local Gemma and Qwen models to triage OpenClaw issues and pull requests]() Original publisher: [Read original article]() Author: Onur Solmaz; ben burtenshaw; shaun smith Published: 2026-06-22T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [OpenClaw](), [Agent Harness](), [Open Source](), [Artificial Intelligence](), [gemma](), [qwen]() Tags: [agent](), [agent-harness](), [artificial-intelligence](), [free](), [gemma](), [guide](), [open-source](), [open-source-collab](), [openclaw](), [pull-requests](), [qwen](), [real-time]() ### AI overview The article describes using local Gemma and Qwen models in an agent harness to classify and triage issues and pull requests in the OpenClaw repository. It presents local execution as a way to support near-real-time notifications without relying on a paid hosted-model quota, using structured outputs and a finite label set. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Safetensors is Joining the PyTorch Foundation DevFeed: [Safetensors is Joining the PyTorch Foundation]() Original publisher: [Read original article]() Author: Luc Georges; Lysandre Published: 2026-04-08T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [PyTorch](), [Open Source](), [Open Source Models & Datasets](), [hugging face](), [Maintainers](), [Malware](), [JSON]() Tags: [community](), [contributors](), [data](), [hugging-face](), [json](), [maintainers](), [models](), [open-source](), [open-source-collab](), [partnerships](), [pytorch](), [safety]() ### AI overview Safetensors is joining the PyTorch Foundation, bringing its vendor-neutral governance under the Linux Foundation while preserving the existing format, APIs, Hub integration, and model compatibility. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## mmBERT: ModernBERT goes Multilingual DevFeed: [mmBERT: ModernBERT goes Multilingual]() Original publisher: [Read original article]() Author: Marc Marone; Orion Weller; William Fleshman; Eugene Yang; Dawn Lawrie; Ben Van Durme Published: 2025-09-09T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Artificial Intelligence](), [Training AI Models](), [Machine Learning & Artificial Intelligence](), [datasets](), [Open Source Models & Datasets](), [Data Quality](), [Boilerplate]() Tags: [artificial-intelligence](), [blog-post](), [community](), [data-quality](), [datasets](), [llm](), [model](), [models](), [nlp](), [open-source](), [open-source-collab](), [performance](), [research](), [speed](), [training]() ### AI overview This blog post introduces mmBERT, a massively multilingual encoder model trained on more than 3 trillion tokens across over 1,800 languages. It describes performance and speed improvements over earlier multilingual models, the model's ModernBERT-based architecture, and a progressive strategy for adding languages and balancing training data. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Ettin Suite: SoTA Paired Encoders and Decoders DevFeed: [Ettin Suite: SoTA Paired Encoders and Decoders]() Original publisher: [Read original article]() Author: Orion Weller; K Ricci; Marc Marone; Antoine Chaffin; Dawn Lawrie; Ben Van Durme Published: 2025-07-16T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Artificial Intelligence](), [Machine Learning & Artificial Intelligence](), [Open Source](), [Large Language Model](), [Model Development](), [datasets]() Tags: [artificial-intelligence](), [bert](), [community](), [comparisons](), [datasets](), [embedding](), [llama](), [llm](), [model-development](), [nlp](), [open-source](), [open-source-collab](), [qwen](), [research](), [retrieval](), [training]() ### AI overview The article introduces Ettin, a suite of paired encoder-only and decoder-only language models ranging from 17M to 1B parameters. The models are trained with identical data, architectures, and recipes, enabling controlled comparisons between masked and causal language modeling. Ettin reports state-of-the-art performance for open-data models and explores converting models between encoder and decoder architectures. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## HuggingFace, IISc partner to supercharge model building on India's diverse languages DevFeed: [HuggingFace, IISc partner to supercharge model building on India's diverse languages]() Original publisher: [Read original article]() Author: Prasanta Kumar Ghosh; Nihar Desai; Sanka; Sujith Pulikodan Published: 2025-02-27T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [datasets](), [hugging face](), [AI Development](), [AI Chat](), [Language models](), [Open Source]() Tags: [ai](), [audio](), [community](), [dataset](), [google](), [hugging-face](), [india](), [language-models](), [open-source](), [open-source-collab](), [partnership](), [speech](), [transcription]() ### AI overview Hugging Face and IISc/ARTPARK are partnering to improve access to and usability of Project Vaani, an open-source multimodal dataset representing India's linguistic diversity. The dataset includes speech and transcribed text from languages and dialects across the country's districts, supporting speech recognition, language modeling, segmentation, and other AI applications. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Visual Document Retrieval Goes Multilingual DevFeed: [Visual Document Retrieval Goes Multilingual]() Original publisher: [Read original article]() Author: Marco Cimolai; Logan Markewich Published: 2025-01-10T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [datasets](), [Embeddings](), [hugging face](), [llamaindex](), [multimodal](), [Open Source](), [Inference](), [data](), [Query (disambiguation)]() Tags: [artificial-intelligence](), [benchmarks](), [cv](), [data](), [datasets](), [embeddings](), [hugging-face](), [inference](), [integrations](), [llamaindex](), [multimodal](), [nlp](), [open-source](), [open-source-collab](), [retrieval](), [synthetic](), [vector](), [vlms]() ### AI overview The article introduces a multilingual embedding model for visual document retrieval and its English-only counterpart. The models encode document page screenshots into dense single-vector representations, enabling visual search across languages without OCR or document-chunking pipelines. The article also presents a 500,000-sample open-source multilingual synthetic dataset, reports faster inference and lower VRAM usage, and describes cross-lingual retrieval and Matryoshka Representation Learning capabilities. ### 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. ## 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. ## Blazing Fast SetFit Inference with 🤗 Optimum Intel on Xeon DevFeed: [Blazing Fast SetFit Inference with 🤗 Optimum Intel on Xeon]() Original publisher: [Read original article]() Author: Daniel Korat; Tom Aarsen; Oren Pereg; Moshe Wasserblat; Ella Charlaix; Abirami Prabhakaran Published: 2024-04-03T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [Post-training optimization]() Tags: [artificial-intelligence](), [collaboration](), [community](), [deep-learning](), [deployment](), [embeddings](), [inference](), [intel](), [nlp](), [open-source](), [open-source-collab](), [optimum](), [pytorch](), [quantization]() ### AI overview A tutorial on accelerating SetFit inference on Intel Xeon CPUs with Optimum Intel and post-training quantization. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science.