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Amazon Sagemaker

Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.

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Solar Pro Preview

By:
Upstage
The most intelligent LLM on a single GPU
  • This product has been removed and is no longer available to new customers.

Product Overview

Solar Pro Preview is a sneak peek at November’s full version of Solar Pro, a state-of-the-art model designed to deliver exceptional performance on a single GPU. This model enables businesses to harness the power of AI without having to overhaul their infrastructure or send their data to external APIs. Currently, Solar Pro Preview supports only English. The official version of Solar Pro will be released this November 2024 with expanded language support beyond English and longer context windows.

Key Data

By
Upstage
Categories
Natural Language Processing
Generative AI
Generation-Text
Type
Model Package
Fulfillment Methods
Amazon SageMaker

Highlights

  • Among models that can be deployed on a single GPU, Solar Pro Preview shows superior performance in intelligence and instruction-following capabilities, as measured by MMLU-Pro and IFEval scores respectively. Additionally, Solar Pro Preview demonstrated comparable performance even against models requiring multiple GPUs, such as Llama 3.1 70B.

  • Our API is fully compatible with OpenAI's interface, allowing for easy integration by developers familiar with OpenAI. It maintains similar structures and methods, enabling a smooth transition with minimal code changes. This compatibility covers various AI functionalities while also offering our unique features, providing a familiar yet enhanced environment for AI-driven applications.

  • Instruction Following, Natural Language Understanding, Natural Language Generation, Question Answering, Text Generation and Editing, Translation, Summarization, Information Extraction, Chatbots and Conversational Interfaces, Educational and Learning Assistance, Data Analysis and Report Writing, Code Generation and Debugging, Content Recommendation Systems, Sentiment Analysis and Customer Feedback Processing

Not quite sure what you’re looking for? AWS Marketplace can help you find the right solution for your use case. Contact us

Usage Information

Model input and output details

Input

Summary

We support a request payload that is compatible with OpenAI's chat completion endpoint.

Limitations for input type
Solar Pro Preview supports a maximum context of 4k (4096) tokens for input and generated tokens.
Input MIME type
application/json
Sample input data
{
    "model": "solar-pro",
    "messages": [
      {
        "role": "system",
        "content": "You are a helpful assistant."
      },
      {
        "role": "user",
        "content": "Can you provide a Python script to merge two sorted lists?"
      }
    ],
    "temperature": 0.7,
}
Expand all input descriptions
Field name
model
Description
Name of the model. Always 'solar-pro'.
Constraints

Type: FreeText

Required
Yes
Field name
messages
Description
List of messages that contains role and content. Role must be one of [system, user, assistant].
Constraints

Type: FreeText Limitations: You must provide list of messages that contains role and content. Role must be one of [system, user, assistant].

Required
Yes
Field name
frequency_penalty
Description
A value between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text, reducing the model's likelihood of repeating the same content verbatim.
Constraints

Default value: 0.0 Type: Continuous Minimum: -2.0 Maximum: 2.0

Required
No
Field name
presence_penalty
Description
A value between -2.0 and 2.0. Positive values penalize new tokens based on their presence in the existing text, increasing the model's likelihood of introducing new topics.
Constraints

Default value: 0.0 Type: Continuous Minimum: -2.0 Maximum: 2.0

Required
No
Field name
max_tokens
Description
The maximum number of tokens that can be generated in the chat completion. Solar Pro supports a maximum context of 4k(4096) tokens for input and generated tokens.
Constraints

Default value: 16 Type: Integer Minimum: 0 Maximum: 4096

Required
No
Field name
temperature
Description
The sampling temperature to use, ranging from 0 to 2. Higher values (e.g., 0.8) increase randomness in the output, while lower values (e.g., 0.2) produce more focused and deterministic results.
Constraints

Default value: 1.0 Type: Continuous Minimum: 0.0 Maximum: 2.0

Required
No
Field name
top_p
Description
Nucleus sampling is an alternative to temperature sampling. It considers only the tokens comprising the top p probability mass. For example, a top_p value of 0.1 means only the tokens making up the top 10% probability mass are considered.
Constraints

Default value: 1.0 Type: Continuous Minimum: 0.0 Maximum: 1.0

Required
No
Field name
stream
Description
Specifies whether to stream the response.
Constraints

Default value: false Type: Categorical Allowed values: true,false

Required
No

Output

Summary

We support the response payload compatible with OpenAI's Chat completion endpoint.

Output MIME type
application/json
Sample output data
{
  "id": "cmpl-cc3f71b58086441fa6a53480b94fd0ec",
  "object": "chat.completion",
  "created": 3421,
  "model": "solar-pro",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "What do you call a fake noodle?\n\nAn impasta!"
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 34,
    "total_tokens": 334,
    "completion_tokens": 300
  }
}
Expand all output descriptions
Field name
id
Description
An ID of the completion. Type: FreeText
Always returned
Yes
Data type
Field name
object
Description
The type of completion object. Always chat.completion. Type: FreeText
Always returned
Yes
Data type
Field name
created
Description
A timestamp indicating when the response was created. Type: Integer
Always returned
Yes
Data type
Field name
model
Description
The name of the model used by completion. Type: FreeText
Always returned
Yes
Data type
Field name
choices
Description
A list of chat completions, including index, message, and finish_reason. Type: FreeText
Always returned
Yes
Data type
Field name
usage
Description
The number of tokens used in generating the completion, including prompt tokens, completion tokens, and the total token count. Type: Integer
Always returned
Yes
Data type

Sample notebook

Additional Resources

End User License Agreement

By subscribing to this product you agree to terms and conditions outlined in the product End user License Agreement (EULA)

Support Information

Solar Pro Preview

Contact us for model fine-tuning request. https://www.upstage.ai/llm/custom-model-request

AWS Infrastructure

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Refund Policy

This product is offered for free. If there are any questions, please contact us for further clarifications.

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