# cohere Published articles for cohere. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## "Machine translation is still broken for most of the world's languages": Cohere builds non-reasoning for a reason DevFeed: ["Machine translation is still broken for most of the world's languages": Cohere builds non-reasoning for a reason]() Original publisher: [Read original article]() Author: Adrian Bridgwater Published: 2026-09-13T14:21:46Z Content type: news Language: en Sources: [The New Stack]() Topics: [Mixture of Experts (MoE)](), [cohere](), [Artificial Intelligence](), [Benchmark](), [Inference](), [qwen](), [gemma4](), [Google]() Tags: [ai](), [ai-infrastructure](), [ai-models](), [aya](), [benchmark](), [cohere](), [gemma](), [google](), [inference](), [large-language-models](), [mixture-of-experts-moe](), [model](), [open](), [qwen]() ### AI overview Cohere's North Small Translate is an open-weight mixture-of-experts machine translation model covering 50 languages. The article discusses its non-reasoning design, sovereign AI positioning, deployment options, efficiency claims, and reported WMT26 benchmark comparisons. ### Source excerpt Enterprise AI company Cohere announced North Small Translate last week, a mixture-of-experts (MOE) open-weight machine translation model that works across The post "Machine translation is still broken for most of the world's languages": Cohere builds non-reasoning for a reason appeared first on The New Stack. ## Cohere's new translation model is open weights -- but not for commercial use DevFeed: [Cohere's new translation model is open weights -- but not for commercial use]() Original publisher: [Read original article]() Author: Meredith Shubel Published: 2026-09-11T17:50:11Z Content type: news Language: en Sources: [The New Stack]() Topics: [Inference](), [moe]() Tags: [ai](), [ai-infrastructure](), [ai-models](), [ai-strategy](), [api](), [cohere](), [hugging-face](), [inference](), [mixture-of-experts](), [model](), [models](), [open](), [production](), [release]() ### AI overview Cohere released North Small Translate 1.0 as open weights under CC BY-NC 4.0, allowing download, evaluation, and study but requiring a commercial agreement for production use. Commercial deployment requires a license and use of Cohere's managed Model Vault platform. ### Source excerpt This week, Cohere released North Small Translate 1.0 under a CC BY-NC 4.0 license: the weights are there to download, The post Cohere's new translation model is open weights -- but not for commercial use appeared first on The New Stack. ## Heroku AI adds a flexible standard plan and expands its managed model catalog DevFeed: [Heroku AI adds a flexible standard plan and expands its managed model catalog]() Original publisher: [Read original article]() Author: Anush DSouza Published: 2026-02-19T17:08:20Z Content type: release Language: en Sources: [Heroku]() Topics: [Heroku AI](), [Heroku](), [model-deployment](), [Claude](), [deepseek](), [cohere]() Tags: [claude](), [cohere](), [deepseek](), [deployment](), [heroku](), [heroku-ai](), [inference](), [managed-inference-and-agents](), [models](), [news](), [updates]() ### AI overview Heroku introduces a standard plan for Managed Inference and Agents that lets developers access supported models through one add-on and API key, while adding Claude 4.6, new open-weight models, and Cohere Embed V4. ### Source excerpt Heroku is introducing significant updates to Managed Inference and Agents. These changes focus on reducing developer friction, expanding model catalogue, and streamlining deployment workflows. More flexibility with the new standard plan Until now, Heroku's model-based plans required developers to provision a specific add-on for a specific model. This created significant operational overhead. If you wanted [...] The post Whats New in Heroku AI: New Models and a Flexible Standard Plan appeared first on Heroku. ## Redpanda open-sources top 16 AI connectors DevFeed: [Redpanda open-sources top 16 AI connectors]() Original publisher: [Read original article]() Author: Mike Broberg Published: 2025-08-18T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [Redpanda-Connect](), [Open Source](), [Artificial Intelligence](), [stream-processing](), [OpenAI](), [Streaming](), [Amazon Bedrock](), [Embeddings](), [Retrieval Augmented Generation (RAG)](), [cohere](), [Ollama]() Tags: [ai](), [ai-connector-platforms](), [ai-connectors-for-commercial-products](), [ai-connectors-open-source](), [ai-data-streaming](), [ai-integration-tools](), [announcement](), [bedrock](), [cloud-native-ai-services](), [cohere](), [embeddable-ai-capabilities](), [embeddings](), [ollama](), [open-source](), [open-source-ai-for-business](), [openai](), [openai-connectors](), [rag](), [real-time-ai-streaming](), [redpanda-ai-connectors](), [redpanda-connect](), [stream-processing](), [streaming](), [streaming-ai-processors]() ### AI overview Redpanda announces the open-source release of its top AI connectors under the Apache 2.0 license. The connectors integrate Redpanda Connect with destinations and models including OpenAI, Cohere, Amazon Bedrock, Ollama, and Google Cloud Vertex AI, supporting streaming pipelines and use cases such as generation, summarization, classification, translation, and text embeddings for RAG. ### Source excerpt Redpanda open-sources top AI connectors to the most used destinations, including OpenAI, Cohere, Bedrock, Ollama, and Vertex AI. Learn more. ## Cohere on Hugging Face Inference Providers 🔥 DevFeed: [Cohere on Hugging Face Inference Providers 🔥]() Original publisher: [Read original article]() Author: Vaibhav Srivastav; ben burtenshaw; merve; Célina Hanouti; Alejandro Rodriguez; Julien Chaumond; Simon Brandeis Published: 2025-04-16T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [cohere](), [inference-providers](), [hugging face](), [Retrieval Augmented Generation (RAG)](), [multimodal](), [Open Source]() Tags: [artificial-intelligence](), [cohere](), [embeddings](), [enterprise](), [generative-ai](), [hub](), [hugging-face](), [inference](), [inference-providers](), [llms](), [multimodal](), [open](), [partnerships](), [rag](), [ranking]() ### AI overview Cohere is now supported as an Inference Provider on the Hugging Face Hub, allowing serverless inference for a selection of Cohere and Cohere Labs models. The article highlights models for enterprise AI, multilingual applications, retrieval-augmented generation, tool use, low-cost or low-latency workloads, and vision-language tasks. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## A Deepdive into Aya Vision: Advancing the Frontier of Multilingual Multimodality DevFeed: [A Deepdive into Aya Vision: Advancing the Frontier of Multilingual Multimodality]() Original publisher: [Read original article]() Author: Saurabh Dash; Yiyang Nan; Arash Ahmadian; John Dang Published: 2025-03-04T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [aya](), [vlm](), [cohere](), [Benchmark](), [datasets](), [Open Source](), [Large Language Model]() Tags: [aya](), [benchmark](), [cohere](), [datasets](), [language-models](), [llm](), [multimodal](), [open-source](), [performance](), [synthetic](), [technical](), [techniques](), [text-generation](), [vision](), [vlm]() ### AI overview Cohere For AI introduces Aya Vision, an open-weight multilingual and multimodal model family supporting language and vision understanding across 23 languages. The article describes its training techniques, benchmark results, open-weight releases, and image-processing architecture, including dynamic image tiling and latency improvements. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## A Deepdive into Aya Expanse: Advancing the Frontier of Multilinguality DevFeed: [A Deepdive into Aya Expanse: Advancing the Frontier of Multilinguality]() Original publisher: [Read original article]() Author: John Dang; Shivalika Singh; Daniel D'souza; Arash Ahmadian Published: 2024-10-24T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [aya](), [cohere](), [Large Language Model](), [synthetic-data](), [Training AI Models](), [Fine-tuning]() Tags: [ai](), [announcement](), [aya](), [cohere](), [comparison](), [llm](), [llms](), [open-source](), [research](), [synthetic-data](), [technical](), [training]() ### AI overview The Cohere For AI team presents Aya Expanse, an 8B and 32B multilingual model family designed to improve performance across languages. The article describes data arbitrage, multilingual preference training, safety tuning, model merging, evaluations across 23 languages, and the release of both models as open weights. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Ep. 5: Key Techniques for Accurate AI-Driven Information Retrieval DevFeed: [Ep. 5: Key Techniques for Accurate AI-Driven Information Retrieval]() Original publisher: [Read original article]() Published: 2024-08-22T00:00:00Z Content type: tutorial Language: en Sources: [William Kennedy]() Topics: [information retrieval](), [Embeddings](), [Generative AI](), [Artificial Intelligence](), [context](), [data](), [Databases](), [cohere](), [HTML](), [Website]() Tags: [ai](), [ai-and-web-page-content-extraction](), [ai-driven-information-retrieval](), [ai-models](), [ai-models-and-text-data](), [cohere](), [cohere-api-for-embeddings](), [context](), [context-handling-in-ai](), [cosine-similarity-search](), [data](), [databases](), [effective-ai-information-retrieval](), [embeddings](), [enhancing-ai-model-accuracy](), [generative-ai-techniques](), [guide](), [how-to](), [html](), [information-retrieval](), [lancedb-vector-database](), [large-text-data-processing](), [managing-large-scale-text-data-in-ai](), [open-source](), [semantic-search-optimization](), [vector-embeddings-in-ai](), [vectorization-techniques]() ### AI overview Episode 5 of an introductory Generative AI series explains techniques for retrieving relevant information from large text collections. It covers converting web content to Markdown, splitting text into overlapping chunks to preserve context, generating vector embeddings with Cohere's API, storing embeddings in vector databases such as LanceDB, and using cosine similarity for semantic search. ### Source excerpt Introduction: Welcome to Episode 5 of our Intro to Generative AI series! In this episode, Daniel explores practical techniques for enhancing AI models' ability to handle large volumes of text data effectively. He addresses the challenges developers face when working with extensive content, such as entire web pages or internal documents, and provides actionable strategies to optimize the retrieval and processing of relevant information. Context Handling: Splitting large text into manageable chunks while preserving context. Vectorization Techniques: Converting text chunks into vector representations for semantic search. Semantic Search: Implementing cosine similarity to retrieve relevant information efficiently. ## Chainguard joins Coalition for Secure AI with OpenAI, Google, Anthropic DevFeed: [Chainguard joins Coalition for Secure AI with OpenAI, Google, Anthropic]() Original publisher: [Read original article]() Published: 2024-07-18T00:00:00Z Content type: article Language: en Sources: [Chainguard: Unchained]() Topics: [ai security](), [supply-chain-security](), [Security for AI](), [Security](), [Open Source]() Tags: [ai-security](), [amazon](), [anthropic](), [chainguard](), [cisco](), [coalition-for-secure-ai](), [cohere](), [cosai](), [google](), [ibm](), [intel](), [microsoft](), [nvidia](), [oasis-open](), [openai](), [provenance](), [secure-by-design](), [software-supply-chain-security](), [standards](), [wiz]() ### AI overview Chainguard joins the Coalition for Secure AI (CoSAI) as a founding member alongside major technology companies. The coalition aims to develop open-source methodologies, standardized frameworks, and practical tools for Secure-by-Design AI systems, with initial workstreams covering AI software supply-chain security, cybersecurity integration, and AI security governance. ### Source excerpt Chainguard joins the Coalition for Secure AI with OpenAI, Google, and Anthropic, enhancing AI security. Discover our commitment to safeguarding AI technologies. ## Putting RL back in RLHF DevFeed: [Putting RL back in RLHF]() Original publisher: [Read original article]() Author: Shengyi Costa Huang; Arash Ahmadian Published: 2024-06-12T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [rlhf](), [trl](), [Reinforcement learning](), [dpo](), [cohere]() Tags: [cohere](), [dpo](), [gpu](), [reinforcement-learning](), [research](), [rl](), [rlhf](), [trl]() ### AI overview This article introduces the RLOO Trainer in TRL, an online reinforcement learning algorithm for RLHF designed as a more accessible alternative to PPO. It explains that RLOO uses less GPU memory, converges faster, performs competitively with PPO, and outperforms offline methods such as DPO in the reported comparisons. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## From cloud to developers: Hugging Face and Microsoft Deepen Collaboration DevFeed: [From cloud to developers: Hugging Face and Microsoft Deepen Collaboration]() Original publisher: [Read original article]() Author: Jeff Boudier; Philipp Schmid Published: 2024-05-21T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [hugging face](), [Microsoft](), [Azure](), [Large Language Model](), [Open Source](), [Artificial Intelligence](), [Cloud](), [Deployment](), [optimum](), [rocm](), [llama](), [Testing]() Tags: [artificial-intelligence](), [azure](), [cloud](), [cohere](), [deployment](), [hugging-face](), [large-language-models](), [meta](), [microsoft](), [open-source](), [optimum](), [partnership](), [qwen](), [rocm](), [testing]() ### AI overview Hugging Face and Microsoft expand their collaboration to make open AI models easier to deploy and run on Azure. The article highlights one-click deployment through Azure AI Studio, support for models such as Llama 3, Command R Plus, Qwen 1.5 110B, and Phi-3, and optimization for AMD Instinct MI300X virtual machines using Optimum-AMD and ROCm. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science.