Sold by: CohereÂ
Deployed on AWS
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A fine-tunable version of Command R. Command R is a generative 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. It is built for enterprises that plan to leverage their internal data and documents for a tailored and accurate language model. This instance is fine-tunable, allowing for customization on advanced use cases by leveraging your data.
To access Cohere's Command R Finetuning model, please refer to the Sagemaker listing as Jumpstart is currently not supporting finetuning capabilities. Batch transform is not supported with this model.
Highlights
- Command R with fine-tuning allows you to customize your models to be performant for your business, domain, and industry. Alongside the fine-tuned model, users additionally benefit from Command R’s proficiency in the most commonly used business languages (10 languages) and retrieval-augmented generation (RAG) with citations for accurate and verified information.
- Command R with fine-tuning achieves high levels of performance with less resource usage on targeted use cases. Enterprises will see lower operational costs, improved latency and increased throughput without extensive computational demands.
- It excels at tasks such as: document summarization, content Q&A, long-form generation, and content generation amongst others. With fine-tuning it can power industry and business specific knowledge assistants, chatbots, customer support agents and more.
Details
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Delivery method
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 | $32.89 |
ml.p4de.24xlarge Inference (Real-Time) Recommended | Model inference on the ml.p4de.24xlarge instance type, real-time mode | $32.89 |
ml.p4de.24xlarge Training Recommended | Algorithm training on the ml.p4de.24xlarge instance type | $32.89 |
Vendor refund policy
No refunds. Please contact support+aws@cohere.com for further assistance.
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Delivery details
Amazon SageMaker algorithm
An Amazon SageMaker algorithm is a machine learning model that requires your training data to make predictions. Use the included training algorithm to generate your unique model artifact. Then deploy the 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
Initial release.
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 |
Resources
Vendor resources
Support
Vendor support
Contact us at support+aws@cohere.comÂ
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