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.
IBM Granite 34B Code Instruct - 8K
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v1
IBM Granite 34B Code Instruct 8K is a model fine-tuned to enhance instruction following, logical reasoning, and problem-solving skills.
Product Overview
The IBM Granite 34B Code Instruct 8K is a 34B parameter model with a context window of 8K tokens. Trained on permissively licensed data, it is fine-tuned to enhance instruction following, logical reasoning, and problem-solving skills. The Granite series includes base foundational models designed for code-related tasks such as code repair, explanation, and synthesis, as well as instruct models fine-tuned on Git commits paired with human instructions and open-source, synthetically generated code instruction datasets. These models adhere to IBM's AI Ethics principles, ensuring ethical data handling, and are released under the Apache 2.0 license, making them trustworthy, enterprise-grade solutions. Granite models are available in sizes of 3B, 8B, 20B, and 34B. They have been trained on 116 programming languages and achieve state-of-the-art results in tasks like code generation, explanation, fixing, editing, and translation.
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Highlights
The IBM Granite 34B Code Instruct 8K is a 34B-parameter model with a context window of 8K tokens, fine-tuned to enhance instruction following capabilities including logical reasoning and problem-solving skills. It achieves state-of-the-art results in tasks such as code generation, explanation, fixing, editing, and translation.
The IBM Granite code models, with sizes ranging from 3B to 34B parameters, are developed under IBM's AI Ethics principles, using high-quality data to ensure ethical AI use, and are licensed under Apache 2.0 for both research and commercial purposes.
The IBM Granite instruct models have been fine-tuned on Git commits across 116 programming languages, paired with human instructions and open-source, synthetically generated code instruction datasets.
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Version
Region
Software Pricing
Model Realtime Inference$0.00/hr
running on ml.g5.48xlarge
Model Batch Transform$0.00/hr
running on ml.g5.48xlarge
Infrastructure PricingWith Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
With Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
SageMaker Realtime Inference$20.36/host/hr
running on ml.g5.48xlarge
SageMaker Batch Transform$20.36/host/hr
running on ml.g5.48xlarge
Model Realtime Inference
For model deployment as Real-time endpoint in Amazon SageMaker, the software is priced based on hourly pricing that can vary by instance type. Additional infrastructure cost, taxes or fees may apply.InstanceType | Realtime Inference/hr | |
|---|---|---|
ml.g5.12xlarge | $0.00 | |
ml.g5.48xlarge Vendor Recommended | $0.00 | |
ml.g5.24xlarge | $0.00 |
Usage Information
Model input and output details
Input
Summary
The model can be invoked by passing a prompt. Please see the sample notebook for details.
Input MIME type
application/jsonSample input data
Output
Summary
The model's output is stored in the dictionary under the key "generated_text".
Output MIME type
application/jsonSample output data
Sample notebook
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