Sold by: Cohere
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Command R+ is a generative language model optimized for long-context tasks and large scale production workloads.
Overview
Command R+ is a highly performant generative large language model, optimized for a variety of use cases including reasoning, summarization, and question answering. Command R+ has the capability for multilingual generation evaluated in 10 languages and highly performant RAG capabilities. This variant of the model runs on A100 GPUs.
Highlights
- Command R+ is built for enterprises that plan to leverage their internal data and documents for a tailored and accurate language model. It utilizes retrieval-augmented generation (RAG) to provide accurate and verified information, ensuring credible interactions. Command R+ model outputs come with clear citations. It is proficient in the most commonly used business languages (10 languages), ensuring seamless understanding and response in various tongues.
- **128k Context Window:** Processes a wide context window of up to 128k tokens, optimizing for RAG use cases and performance. **Tool Use:** Build user-defined tools that enable Command R+ to automate highly sophisticated tasks using internal infrastructure as well as external tools like CRMs and search engines.
Details
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Pricing
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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 | $82.89 |
ml.p4de.24xlarge Inference (Real-Time) Recommended | Model inference on the ml.p4de.24xlarge instance type, real-time mode | $82.89 |
Dimensions summary
You pay by the hour based on the instance type you run this model on. Two options are available. The ml.g4dn.12xlarge dimension runs inference in batch mode, which processes grouped requests together. The ml.p4de.24xlarge dimension runs inference in real-time mode, which handles requests as they arrive. Your cost scales with how many host-hours each instance runs. Pick the instance and mode that match your workload. There is no upfront commitment; you are billed for the hours you use.
Top-of-mind questions for buyers
Batch mode on the ml.g4dn.12xlarge instance processes grouped requests together, which suits large jobs run at once. Real-time mode on the ml.p4de.24xlarge instance handles requests as they arrive, which suits interactive or live applications. You pick the mode that matches your workload timing.
You pay per host-hour while the instance runs. When you stop the instance, software charges stop. Note that underlying AWS infrastructure fees, such as storage, may still apply even when the instance is not actively running inference. The software meter tracks running time only.
The model supports retrieval augmented generation with in-line citations, tool use to automate workflows, and multi-step tool use across tasks. It covers ten business languages, including English, French, Spanish, German, Japanese, Korean, Arabic, and Chinese. These capabilities apply regardless of which instance or mode you choose.
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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
arn:aws:iam::455073351313:role/sagemaker_marketplace
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/blob/main/examples/command_r_samples/sample_input.json
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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