Sold by: IBM Data and AIÂ
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IBM Granite 3.1 2B is an open-source model with a 128K token context length, optimized for enterprise AI and multilingual tasks.
Overview
The IBM Granite 3.1 2B Instruct is a 2-billion parameter language model optimized for long-context tasks. It is fine-tuned from Granite-3.1-2B-Base using open-source instruction datasets and internally generated synthetic data. It employs structured chat formats, supervised finetuning, reinforcement learning for model alignment, and model merging techniques. Released under the Apache 2.0 license, it supports 12 languages, including English, German, French, and Chinese, with extensibility for others. The model is designed to respond to general instructions and can be used to build AI assistants for multiple domains, including business applications. It excels in summarization, text classification, extraction, QA, retrieval-augmented generation (RAG), code-related tasks, function-calling, and multilingual dialogues. It excels in long-context processing, including long-document and meeting summarization, as well as question-answering, making it well-suited for enterprise and research use.
Highlights
- The IBM Granite 3.1 models rank among the highest open models in its weight class on the Hugging Face OpenLLM Leaderboard. Additionally, the entire Granite 3.1 family—including dense, MoE, and guardrail models—now supports a 128K token context length, enabling more extensive input handling and better long-range understanding.
- The IBM Granite 3.1 models 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 3.1 models support 12 languages, including English, German, Spanish, French, Japanese, and more, with the option to fine-tune for additional languages. Designed for general instruction-following, the models are well-suited for building AI assistants across multiple domains, including business applications.
Details
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Latest version
Deployed on AWS
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Pricing
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Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.g5.12xlarge Inference (Batch) Recommended | Model inference on the ml.g5.12xlarge instance type, batch mode | $0.00 |
ml.p4d.24xlarge Inference (Real-Time) Recommended | Model inference on the ml.p4d.24xlarge instance type, real-time mode | $0.00 |
ml.g5.24xlarge Inference (Batch) | Model inference on the ml.g5.24xlarge instance type, batch mode | $0.00 |
ml.p5.48xlarge Inference (Real-Time) | Model inference on the ml.p5.48xlarge instance type, real-time mode | $0.00 |
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
IBM Granite 3.1 2B Instruct is a 2B-parameter model with 128K token context, trained on permissively licensed multilingual data and synthetic instructions. Supporting 12 languages, it handles tasks like summarization, classification, extraction, QA, RAG, coding, function calls, and multilingual dialogue. It excels in long-context applications including document/meeting summaries and QA, making it suitable for business-focused AI assistants across domains.
Additional details
Inputs
- Summary
The model can be invoked by passing a prompt. Please see the sample notebook for details.
- Input MIME type
- application/json
https://github.com/ibm-granite-community/SageMaker/blob/main/granite-3.1-2b-instruct/real_time_sample_input_data.json
https://github.com/ibm-granite-community/SageMaker/blob/main/granite-3.1-2b-instruct/batch_sample_input_data.json
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
inputs | The prompt to be passed to the model. The prompt template is:
```
System:
[Your instructions]
Question:
[Your question]
Answer:
``` | Type: FreeText | Yes |
Resources
Vendor resources
Support
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