ibm-granite/granite-3.2-2b-instruct
Granite-3.2-2B-Instruct is a 2-billion-parameter instruction-tuned language model developed by IBM, building upon Granite-3.1-2B-Instruct. It features a 32768-token context length and is specifically fine-tuned for enhanced reasoning capabilities, allowing for controllable 'thinking' processes. This model excels at general instruction-following, summarization, text classification, and code-related tasks across 12 supported languages.
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Model Overview
Granite-3.2-2B-Instruct is a 2-billion-parameter, long-context AI model developed by the IBM Granite Team. It is an evolution of Granite-3.1-2B-Instruct, fine-tuned with a combination of permissively licensed open-source datasets and IBM's internally generated synthetic data, specifically designed to improve reasoning tasks. A key feature of this model is its controllable "thinking" capability, which can be activated only when required.
Key Capabilities
- Enhanced Reasoning: Fine-tuned for improved thinking capabilities, with a controllable thought process.
- General Instruction Following: Designed to handle a wide range of instruction-following tasks.
- Multilingual Support: Supports English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese, with potential for finetuning in other languages.
- Diverse NLP Tasks: Proficient in summarization, text classification, text extraction, question-answering, Retrieval Augmented Generation (RAG), and multilingual dialog.
- Code and Function-Calling: Capable of handling code-related tasks and function-calling scenarios.
- Long-Context Processing: Supports long-context tasks, including summarization and QA for extensive documents.
Intended Use Cases
Granite-3.2-2B-Instruct is suitable for integration into AI assistants across various domains, particularly business applications. Its strengths lie in tasks requiring logical thought processes, such as complex problem-solving, and its ability to process long documents makes it valuable for detailed analysis and information extraction.