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As we speak, we’re saying the supply of AI21 Labs’ highly effective new Jamba 1.5 household of enormous language fashions (LLMs) in Amazon Bedrock. These fashions signify a major development in long-context language capabilities, delivering velocity, effectivity, and efficiency throughout a variety of functions. The Jamba 1.5 household of fashions contains Jamba 1.5 Mini and Jamba 1.5 Giant. Each fashions help a 256K token context window, structured JSON output, perform calling, and are able to digesting doc objects.
AI21 Labs is a frontrunner in constructing basis fashions and synthetic intelligence (AI) methods for the enterprise. Collectively, AI21 Labs and AWS are empowering prospects throughout industries to construct, deploy, and scale generative AI functions that resolve real-world challenges and spark innovation by way of a strategic collaboration. With AI21 Labs’ superior, production-ready fashions along with Amazon’s devoted providers and highly effective infrastructure, prospects can leverage LLMs in a safe atmosphere to form the way forward for how we course of data, talk, and study.
What’s Jamba 1.5?
Jamba 1.5 fashions leverage a singular hybrid structure that mixes the transformer mannequin structure with Structured State Space model (SSM) expertise. This revolutionary strategy permits Jamba 1.5 fashions to deal with lengthy context home windows as much as 256K tokens, whereas sustaining the high-performance traits of conventional transformer fashions. You may study extra about this hybrid SSM/transformer structure within the Jamba: A Hybrid Transformer-Mamba Language Model whitepaper.
Now you can use two new Jamba 1.5 fashions from AI21 in Amazon Bedrock:
- Jamba 1.5 Giant excels at complicated reasoning duties throughout all immediate lengths, making it very best for functions that require top quality outputs on each lengthy and quick inputs.
- Jamba 1.5 Mini is optimized for low-latency processing of lengthy prompts, enabling quick evaluation of prolonged paperwork and information.
Key strengths of the Jamba 1.5 fashions embody:
- Lengthy context dealing with – With 256K token context size, Jamba 1.5 fashions can enhance the standard of enterprise functions, resembling prolonged doc summarization and evaluation, in addition to agentic and RAG workflows.
- Multilingual – Assist for English, Spanish, French, Portuguese, Italian, Dutch, German, Arabic, and Hebrew.
- Developer-friendly – Native help for structured JSON output, perform calling, and able to digesting doc objects.
- Pace and effectivity – AI21 measured the efficiency of Jamba 1.5 fashions and shared that the fashions reveal as much as 2.5X quicker inference on lengthy contexts than different fashions of comparable sizes. For detailed efficiency outcomes, go to the Jamba model family announcement on the AI21 website.
Get began with Jamba 1.5 fashions in Amazon Bedrock
To get began with the brand new Jamba 1.5 fashions, go to the Amazon Bedrock console, select Mannequin entry on the underside left pane, and request entry to Jamba 1.5 Mini or Jamba 1.5 Giant.
To check the Jamba 1.5 fashions within the Amazon Bedrock console, select the Textual content or Chat playground within the left menu pane. Then, select Choose mannequin and choose AI21 because the class and Jamba 1.5 Mini or Jamba 1.5 Giant because the mannequin.
By selecting View API request, you will get a code instance of tips on how to invoke the mannequin utilizing the AWS Command Line Interface (AWS CLI) with the present instance immediate.
You may observe the code examples in the Amazon Bedrock documentation to entry obtainable fashions utilizing AWS SDKs and to construct your functions utilizing varied programming languages.
The next Python code instance reveals tips on how to ship a textual content message to Jamba 1.5 fashions utilizing the Amazon Bedrock Converse API for textual content era.
import boto3
from botocore.exceptions import ClientError
# Create a Bedrock Runtime consumer.
bedrock_runtime = boto3.consumer("bedrock-runtime", region_name="us-east-1")
# Set the mannequin ID.
# modelId = "ai21.jamba-1-5-mini-v1:0"
model_id = "ai21.jamba-1-5-large-v1:0"
# Begin a dialog with the consumer message.
user_message = "What are 3 enjoyable details about mambas?"
dialog = [
{
"role": "user",
"content": [{"text": user_message}],
}
]
attempt:
# Ship the message to the mannequin, utilizing a fundamental inference configuration.
response = bedrock_runtime.converse(
modelId=model_id,
messages=dialog,
inferenceConfig={"maxTokens": 256, "temperature": 0.7, "topP": 0.8},
)
# Extract and print the response textual content.
response_text = response["output"]["message"]["content"][0]["text"]
print(response_text)
besides (ClientError, Exception) as e:
print(f"ERROR: Cannot invoke '{model_id}'. Motive: {e}")
exit(1)The Jamba 1.5 fashions are good to be used instances like paired doc evaluation, compliance evaluation, and query answering for lengthy paperwork. They’ll simply evaluate data throughout a number of sources, verify if passages meet particular pointers, and deal with very lengthy or complicated paperwork. You could find instance code within the AI21-on-AWS GitHub repo. To study extra about tips on how to immediate Jamba fashions successfully, try AI21’s documentation.
Now obtainable
AI21 Labs’ Jamba 1.5 household of fashions is mostly obtainable right this moment in Amazon Bedrock within the US East (N. Virginia) AWS Region. Verify the full Region list for future updates. To study extra, try the AI21 Labs in Amazon Bedrock product web page and pricing page.
Give Jamba 1.5 fashions a attempt within the Amazon Bedrock console right this moment and ship suggestions to AWS re:Post for Amazon Bedrock or by way of your traditional AWS Assist contacts.
Go to our community.aws website to search out deep-dive technical content material and to find how our Builder communities are utilizing Amazon Bedrock of their options.
— Antje
