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IBM Granite 3.0 Models
Overview :
IBM Granite 3.0 Models are a series of high-performance AI language models developed by IBM and offered through the Ollama platform. These models are trained on over 12 trillion tokens, demonstrating significant improvements in performance and speed. They support tool-based use cases including retrieval-augmented generation (RAG), code generation, translation, and bug fixing. The IBM Granite 3.0 models include dense models and Mixture of Experts (MoE) models, the latter designed for low-latency use, suitable for on-device applications or scenarios requiring instant inference.
Target Users :
The target audience for IBM Granite 3.0 models includes developers, data scientists, and enterprise users who need to process large amounts of text data, generate code, translate, or fix bugs in software. These models provide efficient text processing capabilities that help users enhance productivity and reduce costs.
Use Cases
Developers can use the Granite 3.0 model to generate code, enhancing development efficiency.
Businesses can use the Granite 3.0 model for text classification to optimize content management systems.
Data scientists can use the Granite 3.0 model for developing question-answering systems to improve user experience.
Features
Text Summarization: Quickly generate summaries of text content.
Text Classification: Classify text content into categories.
Text Extraction: Extract key information from large volumes of text.
Question Answering System: Provide answers to user queries.
Retrieval-Augmented Generation (RAG): Generate text by combining retrieved information.
Code-related: Support code generation, translation, and bug fixing.
Function Invocation: Call specific functions or services.
Multilingual Dialogue: Support dialogues in multiple languages.
How to Use
1. Visit the Ollama platform and select the IBM Granite 3.0 model.
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2. Choose the specific model version based on your needs, such as 2B, 8B, or MoE models.
3. Run the selected model using the command `ollama run granite3-dense` or `ollama run granite3-moe`.
4. Input the relevant text or code based on the model's capabilities for text processing or code generation.
5. Analyze the results produced by the model and make adjustments or optimizations as necessary.
6. Apply the model's output to actual projects or products.
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