Granite 3.0 8b Instruct
Granite 3.0 8B Instruct is a powerful AI model that responds to general instructions and can be used to build AI assistants for various domains. Developed using a combination of open-source and synthetic datasets, this model boasts capabilities such as summarization, text classification, and multilingual dialog use cases. With its decoder-only dense transformer architecture and 8.1 billion parameters, Granite 3.0 8B Instruct is designed to handle complex tasks efficiently. While it's primarily finetuned for English, it can also handle multilingual dialog use cases, although its performance may vary. What makes Granite 3.0 8B Instruct unique is its ability to balance efficiency and accuracy, making it a practical choice for real-world applications.
Deploy Model in Dataloop Pipelines
Granite 3.0 8b Instruct fits right into a Dataloop Console pipeline, making it easy to process and manage data at scale. It runs smoothly as part of a larger workflow, handling tasks like annotation, filtering, and deployment without extra hassle. Whether it's a single step or a full pipeline, it connects with other nodes easily, keeping everything running without slowdowns or manual work.
Table of Contents
Model Overview
The Granite-3.0-8B-Instruct model is a powerful AI tool designed to respond to general instructions and can be used to build AI assistants for multiple domains, including business applications. It’s like having a personal assistant that can help with various tasks!
Capabilities
Capable of handling multilingual dialogues and performing tasks such as summarization, text classification, text extraction, question-answering, and more, this model is a great tool for various applications.
- Multilingual Support: Handles dialogues in 12 languages, including English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese.
- Text Processing Capabilities: Performs tasks such as summarization, text classification, text extraction, question-answering, and more.
- Code-Related Tasks: Helps with code-related tasks, like code completion and function-calling.
Primary Tasks
- Summarization: Summarizes long pieces of text into shorter, more digestible versions.
- Text Classification: Classifies text into different categories, such as spam vs. non-spam emails.
- Text Extraction: Extracts specific information from text, such as names, dates, and locations.
- Question-Answering: Answers questions based on the information provided in the text.
- Retrieval Augmented Generation (RAG): Generates text based on the information retrieved from a database or knowledge graph.
Performance
How fast can this model process information? With its decoder-only dense transformer architecture, it can handle large-scale datasets with ease. The model is trained on a massive 12T tokens, making it well-equipped to handle complex tasks.
- Speed: Handles large-scale datasets with ease.
- Accuracy: Boasts high accuracy in various tasks, including summarization, text classification, text extraction, question-answering, and retrieval augmented generation.
- Efficiency: Designed to be efficient, with a focus on minimizing environmental impact.
Multilingual Support
Supports 12 languages, including English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. While its performance might not be identical across all languages, it’s a great starting point for building AI assistants that cater to diverse linguistic needs.
Limitations
While this model is powerful, it’s not perfect. Let’s talk about some of its limitations.
- Language Limitations: Performance might not be as strong in languages other than English.
- Safety and Bias: Might produce inaccurate, biased, or unsafe responses to user prompts.
- Data Limitations: Trained on a large dataset, but it’s not exhaustive.
- Technical Limitations: Has a sequence length limit of
4096tokens.
Format
This model is based on a decoder-only dense transformer architecture. This means it’s designed to process input text sequences and generate output text sequences.
- Supported Data Formats: Supports text data in multiple languages.
- Input Requirements: Requires input text data in a specific format.
- Output Format: Generates output text sequences in the same format as the input text.
- Special Requirements: Requires a specific set of libraries to be installed.
