aya-expanse-8b
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This repository contains the tokenizer for the Cohere `embed-english-v3.0` model. See our blogpost Cohere Embed V3 for more details on this model. You can use the embedding model either via the Cohere API, AWS SageMaker or in your private deployments. The embedding model can be privately deployed in your AWS Cloud using our AWS SageMaker marketplace offering. It runs privately in your VPC, with latencies as low as 5ms for query encoding.
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This repository contains the tokenizer for the Cohere `embed-multilingual-v3.0` model. See our blogpost Cohere Embed V3 for more details on this model. You can use the embedding model either via the Cohere API, AWS SageMaker or in your private deployments. The embedding model can be privately deployed in your AWS Cloud using our AWS SageMaker marketplace offering. It runs privately in your VPC, with latencies as low as 5ms for query encoding.
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Command A is an open-weights 111B parameter model with a 256k context window focused on delivering great performance across agentic, multilingual, and coding use cases. Compared to other leading proprietary...
Command A+ is Cohere's flagship model for enterprise agentic workflows. It accepts text and image inputs with a 192K context window, supports native tool calling with strict tool schemas, structured...
Command A+ is an open source model with 25 billion active parameters and 218B total parameters model optimized for agentic, multilingual, and reasoning-heavy tasks with a focus on enterprise performance, while also providing support for vision inputs for processing image inputs. Developed by: Cohere and Cohere Labs * Point of Contact: **Cohere Labs** * License: Apache 2.0 * Model: command-a-plus-05-2026 * Model Size: 25B active parameters, 218B total parameters * Context length: 128K input
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Command A+ is an open source model with 25 billion active parameters and 218B total parameters model optimized for agentic, multilingual, and reasoning-heavy tasks with a focus on enterprise performance, while also providing support for vision inputs for processing image inputs. All three quantizations show negligible differences in benchmark quality and performance. **Our recommended quantization for most uses is W4A4 which boasts superior speed and latency characteristics alongside a smaller hardware footprint.** Please install transformers from the source repository that includes the necessary changes for this model.
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Adjust provider, category or input/output filters. New models are added continuously.
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