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In November 2023, we made two new Cohere models available in Amazon Bedrock (Cohere Command Light and Cohere Embed English). Right this moment, we’re asserting the addition of two more Cohere models in Amazon Bedrock; Cohere Command R and Command R+.
Organizations want generative artificial intelligence (generative AI) fashions to securely work together with info saved of their enterprise knowledge sources. Each Command R and Command R+ are highly effective, scalable large language models (LLMs), purpose-built for real-world, enterprise-grade workloads. These fashions are multilingual and are targeted on balancing excessive effectivity with sturdy accuracy to excel at capabilities reminiscent of Retrieval-Augmented Generation (RAG), and power 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 concentrating on RAG and power 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 purposes. Command R+ is optimized for superior RAG to supply enterprise-ready, extremely dependable, and verifiable responses because of in-line citations which come normal 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 info to help duties throughout enterprise capabilities like finance, human assets (HR), gross sales, advertising and marketing, and buyer help, amongst others, in a spread of enterprise sectors. Tool use can be out there in Command R+. Command R+ is a powerful multilingual mannequin, and equally to Command R, includes a tokenizer that compresses non-English textual content a lot better than the tokenizer used for different fashions available in the market.
Getting began with Command R and Command R+
To get began with each fashions in Amazon Bedrock, you first must 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+ – supplying you with higher selection and adaptability to make use of the optimum fashions in your particular enterprise wants.
Upon getting entry to your most well-liked mannequin, you should 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 user 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+
You too can use AWS Command Line Interface (CLI) and AWS Software Development Kit (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(checklist(map(lambda x: f"{x['modelName']} : { x['modelId'] }", listModels['modelSummaries']))))Operating this code offers the checklist:
Command : cohere.command-text-v14
Command Mild : 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 checklist, I choose cohere.command-r-v1:0 mannequin ID and write the code to generate the textual content as proven earlier on this submit.
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 = "software/json"
contentType = "software/json"
response = bedrock.invoke_model(
physique=physique,
modelId=modelId,
settle for=settle for,
contentType=contentType
)
print(json.masses(response.get('physique').learn()))You will 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, are available today in Amazon Bedrock within the US East (N. Virginia) and US West (Oregon) Areas; examine the full Region list for future updates.
Go to our community.aws site to seek 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 attempt within the Amazon Bedrock console as we speak and ship suggestions to AWS re:Post for Amazon Bedrock or by means of your traditional AWS Assist contacts.
– Veliswa.
