OpenAI open weight fashions now obtainable on AWS

AWS is dedicated to bringing you essentially the most superior basis fashions (FMs) within the trade, constantly increasing our choice to incorporate groundbreaking fashions from main AI innovators so that you simply at all times have entry to the most recent developments to drive what you are promoting ahead.

At the moment, I’m comfortable to announce the supply of two new OpenAI fashions with open weights in Amazon Bedrock and Amazon SageMaker JumpStart. OpenAI gpt-oss-120b and gpt-oss-20b fashions are designed for textual content technology and reasoning duties, providing builders and organizations new choices to construct AI purposes with full management over their infrastructure and information.

These open weight fashions excel at coding, scientific evaluation, and mathematical reasoning, with efficiency similar to main alternate options. Each fashions assist a 128K context window and supply adjustable reasoning ranges (low/medium/excessive) to match your particular use case necessities. The fashions assist exterior instruments to boost their capabilities and can be utilized in an agentic workflow, for instance, utilizing a framework like Strands Brokers.

With Amazon Bedrock and Amazon SageMaker JumpStart, AWS provides you the liberty to innovate with entry to lots of of FMs from main AI corporations, together with OpenAI open weight fashions. With our complete number of fashions, you may match your AI workloads to the right mannequin each time.

By means of Amazon Bedrock, you may seamlessly experiment with completely different fashions, combine and match capabilities, and swap between suppliers with out rewriting code—turning mannequin alternative right into a strategic benefit that helps you constantly evolve your AI technique as new improvements emerge. These new fashions can be found in Bedrock through an OpenAI-compatible endpoint. You’ll be able to level the OpenAI SDK to this endpoint or use the Bedrock InvokeModel and Converse API.

With SageMaker JumpStart, you may shortly consider, examine, and customise fashions to your use case. You’ll be able to then deploy the unique or the custom-made mannequin in manufacturing with the SageMaker AI console or utilizing the SageMaker Python SDK.

Let’s see how these work in follow.

Getting began with OpenAI open weight fashions in Amazon Bedrock
Within the Amazon Bedrock console, I select Mannequin entry from the Configure and be taught part of the navigation pane. Then, I navigate to the 2 listed OpenAI fashions on this web page and request entry.

Now that I’ve entry, I exploit the Chat/Check playground to check and consider the fashions. I choose OpenAI because the class after which the gpt-oss-120b mannequin.

Utilizing this mannequin, I run the next pattern immediate:

A household has $5,000 to avoid wasting for his or her trip subsequent 12 months. They will place the cash in a financial savings account incomes 2% curiosity yearly or in a certificates of deposit incomes 4% curiosity yearly however with no entry to the funds till the holiday. In the event that they want $1,000 for emergency bills throughout the 12 months, how ought to they divide their cash between the 2 choices to maximise their trip fund?

This immediate generates an output that features the chain of thought used to supply the outcome.

I can use these fashions with the OpenAI SDK by configuring the API endpoint (base URL) and utilizing an Amazon Bedrock API key for authentication. For instance, I set this surroundings variables to make use of the US West (Oregon) AWS Area endpoint (us-west-2) and my Amazon Bedrock API key:

export OPENAI_API_KEY=""
export OPENAI_BASE_URL="https://bedrock-runtime.us-west-2.amazonaws.com/openai/v1"

Now I invoke the mannequin utilizing the OpenAI Python SDK.

from openai import OpenAI

shopper = OpenAI()

response = shopper.chat.completions.create( 
    messages=[{ "role": "user", "content": "Tell me the square root of 42 ^ 3" }],
    mannequin="openai.gpt-oss-120b-1:0",
    stream=False
)

for merchandise in response:
    print(merchandise)

I save the code (test-openai.py file), set up the dependencies, and run the agent domestically:

pip set up openai
python test-openai.py

To construct an AI agent, I can select any framework that helps the Amazon Bedrock API or the OpenAI API. For instance, right here’s the beginning code for Strands Brokers utilizing the Amazon Bedrock API:

from strands import Agent
from strands.fashions import BedrockModel

bedrock_model = BedrockModel(
    model_id="openai.gpt-oss-120b-1:0",
    region_name="us-west-2",
    streaming=False
)

agent = Agent(
    mannequin=bedrock_model
)

agent("Inform me the sq. root of 42 ^ 3")

I save the code (test-strands.py file), set up the dependencies, and run the agent domestically:

pip set up strands-agents
python test-strands.py

When I’m glad with the agent, I can deploy in manufacturing utilizing the capabilities supplied by Amazon Bedrock AgentCore, together with a totally managed serverless runtime and reminiscence and identification administration.

Getting began with OpenAI open weight fashions in Amazon SageMaker JumpStart
Within the Amazon SageMaker AI console, you should utilize OpenAI open weight fashions within the SageMaker Studio. The primary time I do that, I must arrange a SageMaker area. There are alternatives to set it up for a single person (less complicated) or a corporation. For these assessments, I exploit a single person setup.

Within the SageMaker JumpStart mannequin view, I’ve entry to an in depth description of the gpt-oss-120b or gpt-oss-20b mannequin.

I select the gpt-oss-20b mannequin after which deploy the mannequin. Within the subsequent steps, I choose the occasion kind and the preliminary occasion rely. After a couple of minutes, the deployment creates an endpoint that I can then invoke in SageMaker Studio and utilizing any AWS SDKs.

To be taught extra, go to GPT OSS fashions from OpenAI at the moment are obtainable on SageMaker JumpStart within the AWS Synthetic Intelligence Weblog.

Issues to know
The brand new OpenAI open weight fashions at the moment are obtainable in Amazon Bedrock within the US West (Oregon) AWS Area, whereas Amazon SageMaker JumpStart helps these fashions in US East (Ohio, N. Virginia) and Asia Pacific (Mumbai, Tokyo).

Every mannequin comes geared up with full chain-of-thought output capabilities, offering you with detailed visibility into the mannequin’s reasoning course of. This transparency is especially invaluable for purposes requiring excessive ranges of interpretability and validation. These fashions provide the freedom to switch, adapt, and customise them to your particular wants. This flexibility permits you to fine-tune the fashions to your distinctive use instances, combine them into your present workflows, and even construct upon them to create new, specialised fashions tailor-made to your trade or utility.

Safety and security are constructed into the core of those fashions, with complete analysis processes and security measures in place. The fashions preserve compatibility with the usual GPT-4 tokenizer.

Each fashions can be utilized in your most well-liked surroundings, whether or not that’s by the serverless expertise of Amazon Bedrock or the in depth machine studying (ML) growth capabilities of SageMaker JumpStart. For details about the prices related to utilizing these fashions and providers, go to the Amazon Bedrock pricing and Amazon SageMaker AI pricing pages.

To be taught extra, see the parameters for the fashions and the chat completions API within the Amazon Bedrock documentation.

Get began at present with OpenAI open weight fashions on AWS within the Amazon Bedrock console or in Amazon SageMaker AI console.

– Danilo