In its latest addition to its Granite family of large language models (LLMs), IBM has unveiled Granite 3.2. This new release focuses on delivering small, efficient, practical artificial intelligence (AI) solutions for businesses.

IBM has continued to update its Granite LLMs line at a rapid rate. Its last release, Granite 3.1, appeared at the end of 2024. That version was essentially an update. This new model, however, adds experimental chain-of-thought (CoT) reasoning capabilities to its bag of tricks. 

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CoT reasoning is an advanced AI technique that enables LLMs to break down complex problems into logical steps. This process is meant to imitate human-like reasoning processes. In theory, this approach significantly enhances an LLM's ability to handle tasks requiring multi-step reasoning, calculation, and decision-making.

In particular, IBM CoT uses a Thought Preference Optimization framework that enhances reasoning across a broad spectrum of instruction-following tasks. Unlike traditional reinforcement learning approaches focused mainly on logic-driven tasks, TPO allows for improved reasoning performance without sacrificing general task effectiveness. This approach helps mitigate common performance trade-offs seen in other models that specialize in reasoning.

So, what does this advance mean for you and me? IBM explained that if you think about giving an AI chatbot a prompt, a process called "prompt chaining", you get a specific answer. For example, with prompt chaining the question "What color is the sky?", you should get the answer "Blue."  

"However, if asked to explain 'Why is the sky blue?' using CoT prompting, the AI would first define what 'blue' means (a primary color), then deduce that the sky appears blue due to the absorption of other colors by the atmosphere. This response demonstrates the AI's ability to construct a logical argument," or the appearance that the LLM is reasoning its way to an answer. 

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CoT is available in the Granite 8B and 2B versions. Developers can toggle reasoning on or off programmatically. This option enables businesses to optimize computational resources based on task complexity. After all, sometimes you want to know what the sky is like without any scientific details. This approach, IBM claims, enables the 8B model to rival the performance of much larger models, such as Claude 3.5 Sonnet and GPT-4o on complex mathematical reasoning tasks.

IBM has also introduced a new two-billion-parameter Vision Language Model (VLM), specifically designed for document-understanding tasks. This development is not, as you might first think, a graphics function. Instead, the VLM is meant to improve Granite's document-understanding abilities. IBM used its open-source Docling toolkit to process 85 million PDFs and generated 26 million synthetic question-answer pairs to enhance the VLM's ability to handle complex document-heavy workflows

While other AI companies appear to swerve safety issues, IBM still considers safety a top-of-mind function. Granite Guardian 3.2, the latest in IBM's suite of AI safety models, offers enhanced risk detection in prompts and responses. This updated version maintains performance while reducing model size by 30%, introducing a new "verbalized confidence" feature for more nuanced risk assessment.

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Businesses may also be interested in Granite's advanced forecasting capabilities. The new TinyTimeMixers (TTM) models with sub-10M parameters can run long-term forecasting up to two years into the future. These models are useful for trend analysis in finance, economics, and supply chain management. These models might not help you assemble your fantasy baseball team roster yet, but give them time. 

As before, IBM is the most open-source friendly AI company. All Granite 3.2 models are available under the Apache 2.0 license on Hugging Face. Some models are available on platforms, including IBM WatsonX.ai, Ollama, Replicate, and LM Studio. This open approach aligns with IBM's strategy to make AI more accessible and cost-effective for enterprises.

As Sriram Raghavan, IBM AI research VP, emphasized: "The next era of AI is about efficiency, integration, and real-world impact -- where enterprises can achieve powerful outcomes without excessive spend on compute."

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