An anonymous reader quotes a report from ZDNet: IBM managed the open sourcing of Granite code by using pretraining data from publicly available datasets, such as GitHub Code Clean, Starcoder data, public code repositories, and GitHub issues. In short, IBM has gone to great lengths to avoid copyright or legal issues. The Granite Code Base models are trained on 3- to 4-terabyte tokens of code data and natural language code-related datasets. All these models are licensed under the Apache 2.0 license for research and commercial use. It's that last word -- commercial -- that stopped the other major LLMs from being open-sourced. No one else wanted to share their LLM goodies.
But, as IBM Research chief scientist Ruchir Puri said, "We are transforming the generative AI landscape for software by releasing the highest performing, cost-efficient code LLMs, empowering the open community to innovate without restrictions." Without restrictions, perhaps, but not without specific applications in mind. The Granite models, as IBM ecosystem general manager Kate Woolley said last year, are not "about trying to be everything to everybody. This is not about writing poems about your dog. This is about curated models that can be tuned and are very targeted for the business use cases we want the enterprise to use. Specifically, they're for programming."
These decoder-only models, trained on code from 116 programming languages, range from 3 to 34 billion parameters. They support many developer uses, from complex application modernization to on-device memory-constrained tasks. IBM has already used these LLMs internally in IBM Watsonx Code Assistant (WCA) products, such as WCA for Ansible Lightspeed for IT Automation and WCA for IBM Z for modernizing COBOL applications. Not everyone can afford Watsonx, but now, anyone can work with the Granite LLMs using IBM and Red Hat's InstructLab.
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