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In November 2023, we made two new Cohere fashions accessible in Amazon Bedrock (Cohere Command Gentle and Cohere Embed English). Right this moment, we’re asserting the addition of two extra Cohere fashions in Amazon Bedrock; Cohere Command R and Command R+.
Organizations want generative synthetic intelligence (generative AI) fashions to securely work together with data saved of their enterprise information sources. Each Command R and Command R+ are highly effective, scalable giant language fashions (LLMs), purpose-built for real-world, enterprise-grade workloads. These fashions are multilingual and are centered on balancing excessive effectivity with robust accuracy to excel at capabilities reminiscent of Retrieval-Augmented Era (RAG), and gear use to allow enterprises to maneuver past proof-of-concept (POC), and into manufacturing utilizing synthetic intelligence (AI).
Command R is a scalable multilingual generative mannequin focusing on RAG and gear use to allow production-scale AI for enterprises. Command R+ is a state-of-the-art RAG-optimized mannequin designed to sort out enterprise-grade workloads and optimize enterprise AI functions. Command R+ is optimized for superior RAG to supply enterprise-ready, extremely dependable, and verifiable responses resulting from in-line citations which come customary with this mannequin. With these new Cohere fashions in Bedrock, you’ll be able to scale with AI to shortly find probably the most related data to help duties throughout enterprise features like finance, human sources (HR), gross sales, advertising, and buyer help, amongst others, in a variety of enterprise sectors. Instrument use can also be accessible in Command R+. Command R+ is a powerful multilingual mannequin, and equally to Command R, contains a tokenizer that compresses non-English textual content a lot better than the tokenizer used for different fashions out there.
Getting began with Command R and Command R+
To get began with each fashions in Amazon Bedrock, you first have to get entry to the fashions. Within the Amazon Bedrock console, select Mannequin entry, after which select Handle mannequin entry. Subsequent, select your most well-liked mannequin(s), after which select Save modifications. As you’ll be able to see, you now have six Cohere fashions to select from in Amazon Bedrock – together with Command R and Command R+ – providing you with better alternative and suppleness to make use of the optimum fashions on your particular enterprise wants.
After getting entry to your most well-liked mannequin, you need to use the mannequin in Amazon Bedrock. Refresh the bottom fashions desk to view the up to date standing.
The fashions have been educated to reply within the language of the consumer reminiscent of English, French, Spanish, Italian, German, Brazilian Portuguese, Japanese, Korean, Simplified Chinese language, and Arabic. Right here’s an instance:
Immediate
<s>"Écris une description de produit pour une voiture électrique en 50 à 75 mots"Output
Découvrez la voiture électrique qui va révolutionner votre façon de conduire.
Avec son design élégant, cette voiture offre une expérience de conduite distinctive
avec une accélération puissante et une autonomie impressionnante. Sa
technologie avancée vous garantit une cost rapide et une fiabilité inégalée.
Avec sa conception innovante et sturdy, cette voiture est parfaite pour les
trajets urbains et les longues distances. Profitez d'une conduite silencieuse
et vivez l'expérience de la voiture électrique!Programmatically work together with Command R and Command R+
It’s also possible to use AWS Command Line Interface (CLI) and AWS Software program Growth Package (SDK) to make varied calls utilizing Amazon Bedrock APIs. Following, is a pattern code in Python that interacts with Amazon Bedrock Runtime APIs with AWS SDK. Taking the identical textual content era immediate I used earlier, right here is the way it appears when used programmatically. On this instance I’m interacting with the Command R mannequin. Again to Python, I first run the ListFoundationModels API name to find the modelId for Command R.
import boto3
import json
import numpy
bedrock = boto3.shopper(service_name="bedrock", region_name="us-east-1")
listModels = bedrock.list_foundation_models(byProvider="cohere")
print("n".be part of(listing(map(lambda x: f"{x['modelName']} : { x['modelId'] }", listModels['modelSummaries']))))Working this code offers the listing:
Command : cohere.command-text-v14
Command Gentle : cohere.command-light-text-v14
Embed English : cohere.embed-english-v3
Embed Multilingual : cohere.embed-multilingual-v3
Command R: cohere.command-r-v1:0
Command R+: cohere.command-r-plus-v1:0From this listing, I choose cohere.command-r-v1:0 mannequin ID and write the code to generate the textual content as proven earlier on this put up.
import boto3
import json
bedrock = boto3.shopper(service_name="bedrock-runtime", region_name="us-east-1")
immediate = """
<s>Écris une description de produit pour une voiture électrique en 50 à 75 mots
physique = json.dumps({
"immediate": immediate,
"max_tokens": 512,
"top_p": 0.8,
"temperature": 0.5,
})
modelId = "cohere.command-r-v1:0"
settle for = "utility/json"
contentType = "utility/json"
response = bedrock.invoke_model(
physique=physique,
modelId=modelId,
settle for=settle for,
contentType=contentType
)
print(json.hundreds(response.get('physique').learn()))You may get JSON formatted output as like:
Découvrez la voiture électrique qui va révolutionner votre façon de conduire.
Avec son design élégant, cette voiture offre une expérience de conduite distinctive
avec une accélération puissante et une autonomie impressionnante. Sa
technologie avancée vous garantit une cost rapide et une fiabilité inégalée.
Avec sa conception innovante et sturdy, cette voiture est parfaite pour les
trajets urbains et les longues distances. Profitez d'une conduite silencieuse
et vivez l'expérience de la voiture électrique!Now Out there
Command R and Command R+ fashions, together with different Cohere fashions, can be found in the present day in Amazon Bedrock within the US East (N. Virginia) and US West (Oregon) Areas; verify the full Area listing for future updates.
Go to our group.aws website to search out deep-dive technical content material and to find how our Builder communities are utilizing Amazon Bedrock of their options. Give Command R and Command R+ a strive within the Amazon Bedrock console in the present day and ship suggestions to AWS re:Put up for Amazon Bedrock or via your common AWS Assist contacts.
– Veliswa.