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 3.0 8B Instruct
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v1
IBM's Granite 3.0 8B Instruct is an 8B-parameter AI model for enterprise use, excelling in multilingual and code tasks; Apache 2.0 licensed.
Product Overview
IBM's Granite 3.0 8B Instruct is an 8-billion-parameters LLM with a context window of 4K tokens designed for enterprise applications. It excels in tasks such as retrieval-augmented generation, classification, summarization, entity extraction, and tool utilization. Its compact size ensures efficient performance, making it suitable for integration into diverse business workflows. Trained on over 12 trillion tokens across 12 natural languages and 116 programming languages, Granite 3.0 8B Instruct offers robust multilingual and code understanding capabilities. This third-generation model matches or outperforms similarly sized models on industry benchmarks, emphasizing performance, transparency, and safety. Adhering to IBM's AI Ethics principles and open-source commitment, Granite 3.0 8B Instruct is released under the permissive Apache 2.0 license, making it unique in the combination of performance, flexibility and autonomy it provides to enterprise clients and the community at large.
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Highlights
IBM's Granite 3.0 8B Instruct model is a compact, versatile AI language model designed for enterprise applications. It excels in tasks such as Retrieval Augmented Generation (RAG), classification, summarization, entity extraction, and tool utilization. This compact, versatile model is designed to be fine-tuned with enterprise data and seamlessly integrated across diverse business environments or workflows.
Released under the permissive Apache 2.0 license, the Granite 3.0 8B Instruct model offers enterprises flexibility and autonomy in deployment. IBM provides intellectual property indemnity for all Granite models on watsonx.ai, enhancing confidence for businesses integrating their data with the model.
The Granite 3.0 8B Instruct model demonstrates strong performance across various benchmarks. Trained on over 12 trillion tokens from 12 natural languages and 116 programming languages, it utilizes a novel two-stage training method to optimize data quality and training parameters. This approach ensures robust performance in enterprise AI tasks.
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Pricing Information
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Version
Region
Software Pricing
Model Realtime Inference$0.00/hr
running on ml.p4d.24xlarge
Model Batch Transform$0.00/hr
running on ml.g5.24xlarge
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$37.688/host/hr
running on ml.p4d.24xlarge
SageMaker Batch Transform$10.18/host/hr
running on ml.g5.24xlarge
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.p4d.24xlarge Vendor Recommended | $0.00 | |
ml.p5.48xlarge | $0.00 |
Usage Information
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application/json, text/csv, application/jsonlinesSample input data
Output
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The model's output is stored in the dictionary under the key "generated_text".
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application/json, text/csv, application/jsonlinesSample output data
Sample notebook
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