Amazon Sagemaker
Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.

Cohere Command R 082024 Finetuning Free trial
By:
Latest Version:
v1.0.0
Fine-tunable Cohere Command R 082024 with 16k context and multi-LoRA, optimized for long-context tasks and large-scale production.
Product Overview
Fine-tunable Cohere Command R 082024 with 16k context and multi-LoRA, optimized for long-context tasks and large-scale production.
Key Data
Version
By
Type
Algorithm
Highlights
Cohere Command R 082024 finetuning 16k context length support for training 128k context length support for inference
Not quite sure what you’re looking for? AWS Marketplace can help you find the right solution for your use case. Contact us
Pricing Information
Use this tool to estimate the software and infrastructure costs based your configuration choices. Your usage and costs might be different from this estimate. They will be reflected on your monthly AWS billing reports.
Contact us to request contract pricing for this product.
Estimating your costs
Choose your region and launch option to see the pricing details. Then, modify the estimated price by choosing different instance types.
Version
Region
Software Pricing
Algorithm Training$12.89/hr
running on ml.p4de.24xlarge
Model Realtime Inference$12.89/hr
running on ml.p4de.24xlarge
Model Batch Transform$12.89/hr
running on ml.g4dn.12xlarge
Infrastructure PricingWith Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
With Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
SageMaker Algorithm Training$47.1106/host/hr
running on ml.p4de.24xlarge
SageMaker Realtime Inference$47.111/host/hr
running on ml.p4de.24xlarge
SageMaker Batch Transform$4.89/host/hr
running on ml.g4dn.12xlarge
About Free trial
Try this product for 7 days. There will be no software charges, but AWS infrastructure charges still apply. Free Trials will automatically convert to a paid subscription upon expiration.
Algorithm Training
For algorithm training in Amazon SageMaker, the software is priced based on hourly pricing that can vary by instance type. Additional infrastructure cost, taxes or fees may apply.InstanceType | Algorithm/hr | |
---|---|---|
ml.p4de.24xlarge Vendor Recommended | $12.89 | |
ml.p5.48xlarge | $16.93 |
Usage Information
Training
To train a custom model, please see the example below for parameters to pass to co.finetuning.create_finetuned_model()
, or visit our [API guide] .
Channel specification
Fields marked with * are required
training
*Input modes: File
Content types: -
Compression types: None
evaluation
Input modes: File
Content types: -
Compression types: None
Hyperparameters
Fields marked with * are required
name
*Name of the custom model
Type: FreeText
Tunable: No
train_epochs
Maximum number of training epochs to run for
Type: Integer
Tunable: No
learning_rate
The learning rate
Type: Continuous
Tunable: No
lora_rank
The rank of the LORA tensor
Type: Integer
Tunable: No
train_batch_size
Batch size to use used during training. Should be a multiple of 8
Type: Integer
Tunable: No
early_stopping_enabled
When enabled, stop traininig if the loss metric does not improve beyond 'early_stopping_threshold' for `early_stopping_patience` times of evaluations
Type: Categorical
Tunable: No
early_stopping_patience
Stop traininig if the loss metric does not improve beyond 'early_stopping_threshold' for this many times of evaluations
Type: Integer
Tunable: No
early_stopping_threshold
How much the loss must improve to prevent early stopping
Type: Continuous
Tunable: No
Model input and output details
Input
Summary
You can read about the Hyperparameters to tune here
Input MIME type
application/jsonSample input data
Output
Summary
The output is a JSON object that has the generated text along with likelihoods of tokens, if requested. See example json.
Output MIME type
application/jsonSample output data
Sample notebook
Additional Resources
End User License Agreement
By subscribing to this product you agree to terms and conditions outlined in the product End user License Agreement (EULA)
Support Information
AWS Infrastructure
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
Learn MoreRefund Policy
No refunds. Please contact support+aws@cohere.com for further assistance.
Customer Reviews
There are currently no reviews for this product.
View allWrite a review
Share your thoughts about this product.
Write a customer review