The Archive

Cohere Labs releases Tiny Aya, open-weight multilingual model optimized for on-device inference across 200+ languages.

Cohere educational overview of AI infrastructure components: hardware, software, networking layers for AI stack construction.

Cohere marketing guide covering generative AI use cases and integration strategies for campaigns.

Cohere conceptual overview defining enterprise AI, its business applications, and role in driving automation and growth.

Cohere launches Transcribe speech-to-text API with accuracy/speed claims for audio data search and automation.

Cohere publishes enterprise deployment guidance covering cost, security, and scaling challenges for AI model operations.

Cohere releases Command R7B, compact generative model optimized for speed/efficiency on commodity GPUs and edge devices.

Cohere and NVIDIA partner on NVIDIA-native sovereign AI model for secure, locally-run enterprise deployment.

Cohere appoints chess champion Magnus Carlsen as brand ambassador for company reputation and strategy messaging.

Cohere C-suite guide on enterprise AI advantages: productivity, competitive advantage, and 2026 adoption strategies.

Cohere analysis of AI adoption in financial services: productivity gains, operational efficiency, and implementation pathways.

Cohere webinar on AI applications in financial services; generic promotional content.

Cohere and SAP expand partnership to deploy sovereign AI solutions for European enterprises through SAP Sovereign Cloud.

Cohere, OpenAI, and AI21 Labs have developed a preliminary set of best practices applicable to any organization developing or deploying large language models.

We find that, just as a large transformer model trained on language can generate coherent text, the same exact model trained on pixel sequences can generate coherent image completions and samples. By establishing a correlation between sample quality and image classification accuracy, we show that our best generative model also contains features competitive with top convolutional nets in the unsupervised setting.

We’ve trained a large-scale unsupervised language model which generates coherent paragraphs of text, achieves state-of-the-art performance on many language modeling benchmarks, and performs rudimentary reading comprehension, machine translation, question answering, and summarization—all without task-specific training.