Import customized fashions in Amazon Bedrock (preview)

With Amazon Bedrock, you have got entry to a alternative of high-performing basis fashions (FMs) from main synthetic intelligence (AI) firms that make it simpler to construct and scale generative AI purposes. A few of these fashions present publicly obtainable weights that may be fine-tuned and customised for particular use instances. Nonetheless, deploying personalized FMs in a safe and scalable method just isn’t a simple job.

Beginning as we speak, Amazon Bedrock provides in preview the potential to import customized weights for supported mannequin architectures (equivalent to Meta Llama 2, Llama 3, and Mistral) and serve the customized mannequin utilizing On-Demand mode. You may import fashions with weights in Hugging Face safetensors format from Amazon SageMaker and Amazon Easy Storage Service (Amazon S3).

On this method, you need to use Amazon Bedrock with current personalized fashions equivalent to Code Llama, a code-specialized model of Llama 2 that was created by additional coaching Llama 2 on code-specific datasets, or use your information to fine-tune fashions to your personal distinctive enterprise case and import the ensuing mannequin in Amazon Bedrock.

Let’s see how this works in observe.

Bringing a customized mannequin to Amazon Bedrock
Within the Amazon Bedrock console, I select Imported fashions from the Basis fashions part of the navigation pane. Now, I can create a customized mannequin by importing mannequin weights from an Amazon Easy Storage Service (Amazon S3) bucket or from an Amazon SageMaker mannequin.

I select to import mannequin weights from an S3 bucket. In one other browser tab, I obtain the MistralLite mannequin from the Hugging Face web site utilizing this pull request (PR) that gives weights in safetensors format. The pull request is presently Able to merge, so it is perhaps a part of the principle department while you learn this. MistralLite is a fine-tuned Mistral-7B-v0.1 language mannequin with enhanced capabilities of processing lengthy context as much as 32K tokens.

When the obtain is full, I add the recordsdata to an S3 bucket in the identical AWS Area the place I’ll import the mannequin. Listed here are the MistralLite mannequin recordsdata within the Amazon S3 console:

Again on the Amazon Bedrock console, I enter a reputation for the mannequin and preserve the proposed import job title.

I choose Mannequin weights within the Mannequin import settings and browse S3 to decide on the situation the place I uploaded the mannequin weights.

To authorize Amazon Bedrock to entry the recordsdata on the S3 bucket, I choose the choice to create and use a brand new AWS Identification and Entry Administration (IAM) service function. I take advantage of the View permissions particulars hyperlink to test what can be within the function. Then, I submit the job.

About ten minutes later, the import job is accomplished.

Now, I see the imported mannequin within the console. The checklist additionally exhibits the mannequin Amazon Useful resource Identify (ARN) and the creation date.

I select the mannequin to get extra info, such because the S3 location of the mannequin recordsdata.

Within the mannequin element web page, I select Open in playground to check the mannequin within the console. Within the textual content playground, I kind a query utilizing the immediate template of the mannequin:

<|prompter|>What are the principle challenges to assist an extended context for LLM?</s><|assistant|>

The MistralLite imported mannequin is fast to answer and describe a few of these challenges.

Within the playground, I can tune responses for my use case utilizing configurations equivalent to temperature and most size or add cease sequences particular to the imported mannequin.

To see the syntax of the API request, I select the three small vertical dots on the high proper of the playground.

I select View API syntax and run the command utilizing the AWS Command Line Interface (AWS CLI):

aws bedrock-runtime invoke-model 
--model-id arn:aws:bedrock:us-east-1:123412341234:imported-model/a82bkefgp20f 
--body ">What are the principle challenges to assist an extended context for LLM?</s><" 
--cli-binary-format raw-in-base64-out 
--region us-east-1 
invoke-model-output.txt

The output is much like what I obtained within the playground. As you may see, for imported fashions, the mannequin ID is the ARN of the imported mannequin. I can use the mannequin ID to invoke the imported mannequin with the AWS CLI and AWS SDKs.

Issues to know
You may deliver your personal weights for supported mannequin architectures to Amazon Bedrock within the US East (N. Virginia) AWS Area. The mannequin import functionality is presently obtainable in preview.

When utilizing customized weights, Amazon Bedrock serves the mannequin with On-Demand mode, and also you solely pay for what you utilize with no time-based time period commitments. For detailed info, see Amazon Bedrock pricing.

The flexibility to import fashions is managed utilizing AWS Identification and Entry Administration (IAM), and you may permit this functionality solely to the roles in your group that have to have it.

With this launch, it’s now simpler to construct and scale generative AI purposes utilizing customized fashions with safety and privateness inbuilt.

To study extra:

— Danilo