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Command R+ 08-2024 is a highly performant generative language model optimized for large scale production workloads.
Overview
Command R+ 08-2024 is a powerful generative language model that can be utilized across a broad set of use cases. It is designed to excel at reasoning, summarization, and question answering, and can also be optimized for any industry use case. It is proficient in the 10 most commonly used business languages (Arabic, Mandarin, English, French, German, Italian, Spanish, Japanese, Korean, and Portuguese) and pre-trained on 13 additional languages, ensuring seamless understanding and response in various tongues. It excels at RAG, ensuring strong performance for businesses looking to harness their data and internal knowledge sources in combination with a generative language model. Batch transform is not supported with this model.
Highlights
- Command R+ 08-2024 performs highly across enterprise use cases, especially those utilizing RAG. It can power complex RAG systems, advanced knowledge assistants and more, all grounded in relevant data. It is also performant at long-context tasks, Command R+ 08-2024 has a token context length of 128k that can be used alongside RAG.
- Command R+ 08-2024 is proficient in the 10 most commonly used business languages (Arabic, Mandarin, English, French, German, Italian, Spanish, Japanese, Korean, and Portuguese) and pre-trained on 13 additional languages. The model excels at enterprise oriented tasks such as: document summarization, content Q&A, long-form generation, and content generation amongst others. It can power knowledge assistants, chatbots, customer support agents and more.
- Command R+ 08-2024 allows users to automate highly sophisticated tasks through tool use. Users can build user defined tools to assist in everyday tasks and can perform both single step and multi step actions.
Details
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Latest version
Deployed on AWS
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Pricing
Free trial
Try this product free for 7 days according to the free trial terms set by the vendor.
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.g4dn.12xlarge Inference (Batch) Recommended | Model inference on the ml.g4dn.12xlarge instance type, batch mode | $86.93 |
ml.p5.48xlarge Inference (Real-Time) Recommended | Model inference on the ml.p5.48xlarge instance type, real-time mode | $86.93 |
Vendor refund policy
No refunds. Please contact support+aws@cohere.com for further assistance.
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
We've updated our SageMaker integration with a major version release for Cohere models, including notebook updates. The "/invocation" endpoint now defaults to API V2, ensuring a seamless transition to the latest version. Please see the notebook on how to use this model with the API update: All existing (and future) Command models: https://github.com/cohere-ai/cohere-aws/blob/main/notebooks/sagemaker/Command%20Models.ipynbÂ
Additional details
Inputs
- Summary
The model accepts JSON requests with parameters that can be used to control the generated text. See examples and fields descriptions below.
- Input MIME type
- application/json
https://github.com/cohere-ai/cohere-aws/blob/main/examples/command_r_samples/sample_input.json
https://github.com/cohere-ai/cohere-aws/tree/main/examples/command_r_samples
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
message | Text input for the model to respond to. | Type: FreeText
| Yes |
chat_history | chat_history – (array of messages) A list of previous messages between the user and the model, meant to give the model conversational context for responding to the user's message. Required fields: role – (enum string) Takes “USER” or “CHATBOT”. message – (string) Text contents of the message. | Default value: [] Type: FreeText | No |
documents | A list of texts that the model can cite to generate a more accurate reply. Each document is a string-string dictionary. The resulting generation will include citations that reference some of these documents. It is recommended to keep the total word count of the strings in the dictionary to under 300 words. An `_excludes` field (array of strings) can be optionally supplied to omit some key-value pairs from being shown to the model. | Default value: [] Type: FreeText
| No |
search_queries_only | When `true`, the response will only contain a list of generated search queries, but no search will take place, and no reply from the model to the user's `message` will be generated. | Default value: FALSE Type: Categorical Allowed values: TRUE, FALSE | No |
preamble | Overrides the default preamble for search query generation. Has no effect on tool use generations. | Default value: [] Type: FreeText | No |
stream | When `true`, the response will be a JSON stream of events. The final event will contain the complete response, and will have an `event_type` of `"stream-end"`. Streaming is beneficial for user interfaces that render the contents of the response piece by piece, as it gets generated. | Default value: FALSE Type: Categorical Allowed values: TRUE, FALSE | No |
max_tokens | The maximum number of tokens the model will generate as part of the response. Note: Setting a low value may result in incomplete generations. | Default value: [] Type: Integer Minimum: 0 | No |
temperature | Use a lower value to decrease randomness in the response. Randomness can be further maximized by increasing the value of the `p` parameter. | Default value: 0.3 Type: Continuous Minimum: 0 Maximum: 2
| No |
Top P (p) | Use a lower value to ignore less probable options. Set to 0 or 1.0 to disable. If both p and k are enabled, p acts after k. | Default value: 0.75 Type: Continuous Minimum: 0.01 Maximum: 0.99 | No |
Top K (k) | Specify the number of token choices the model uses to generate the next token. If both p and k are enabled, p acts after k. | Default value: 0 Type: Continuous Minimum: 0 Maximum: 500 | No |
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Contact us at support+aws@cohere.comÂ
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