OpenAI open weight fashions now out there 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 just all the time have entry to the most recent developments to drive your online business ahead.

Right now, I’m completely happy to announce the provision 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 functions with full management over their infrastructure and information.

These open weight fashions excel at coding, scientific evaluation, and mathematical reasoning, with efficiency akin to main alternate options. Each fashions help a 128K context window and supply adjustable reasoning ranges (low/medium/excessive) to match your particular use case necessities. The fashions help exterior instruments to reinforce 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 a whole lot of FMs from main AI corporations, together with OpenAI open weight fashions. With our complete collection of fashions, you may match your AI workloads to the proper mannequin each time.

Via Amazon Bedrock, you may seamlessly experiment with totally different fashions, combine and match capabilities, and change between suppliers with out rewriting code—turning mannequin selection right into a strategic benefit that helps you constantly evolve your AI technique as new improvements emerge. At launch, these new fashions can be found in Bedrock through an OpenAI appropriate 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, evaluate, 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/Take a look at 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 save lots of for his or her trip subsequent yr. They’ll 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 yr, 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 consequence.

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.

shopper = OpenAI()

response = shopper.chat.completion.create(
    messages=[{
        "role": "user",
        "content": "Hello, how are you?"
    }],
    mannequin="openai.gpt-oss-120b-1:0",
    stream=True
)

for merchandise in response:
    print(merchandise)

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
from strands_tools import calculator

mannequin = BedrockModel(
    model_id="openai.gpt-oss-120b-1:0"
)
agent = Agent(
    mannequin=mannequin,
    instruments=[calculator]
)

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

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

pip set up strands-agents strands-agents-tools
python app.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 need to use OpenAI open weight fashions within the SageMaker Studio. The primary time I do that, I have to arrange a SageMaker area. There are alternatives to set it up for a single person (less complicated) or a company. 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 out there on SageMaker JumpStart within the AWS Synthetic Intelligence Weblog.

Issues to know
The brand new OpenAI open weight fashions at the moment are out there 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 outfitted with full chain-of-thought output capabilities, offering you with detailed visibility into the mannequin’s reasoning course of. This transparency is especially beneficial for functions requiring excessive ranges of interpretability and validation. These fashions provide the freedom to change, adapt, and customise them to your particular wants. This flexibility permits you to fine-tune the fashions to your distinctive use circumstances, 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 popular surroundings, whether or not that’s by the serverless expertise of Amazon Bedrock or the intensive machine studying (ML) improvement capabilities of SageMaker JumpStart. For details about the prices related to utilizing these fashions and companies, 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 right this moment with OpenAI open weight fashions on AWS within the Amazon Bedrock console or in Amazon SageMaker AI console.

– Danilo