Granite 4.2 LLMs: How They're Built

IBM’s Granite 4.2 is an Apache 2.0-licensed family of dense, decoder-only reasoning language models available in 3B, 8B, and 30B parameter sizes, designed for instruction following, explicit reasoning, native tool calling, and configurable thinking modes. Built from scratch on about 15 trillion tokens, the models use a five-phase pretraining strategy that expands context capacity to 512K tokens, followed by supervised fine-tuning on roughly 7.2 million instruction, reasoning, coding, multilingual, safety, and agentic samples. Post-training uses a staged reinforcement-learning curriculum based on asynchronous Group Relative Policy Optimization, progressing through verifiable reasoning tasks, targeted instruction and coding improvements, and final preference and safety alignment. The 8B and 30B versions receive additional agentic RL training in real software-engineering, terminal, and web-search environments, enabling them to use tools, edit and execute code, and complete multistep tasks, while the 3B model follows a shorter foundational path. Evaluations report increasing performance with model size across reasoning, coding, instruction following, tool use, and long-context benchmarks, and IBM also provides quantized deployment variants, OpenAI-compatible serving support, and integrations with agentic coding harnesses such as OpenCode, Pi, and OpenHands.

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