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rasbt/reasoning-from-scratch ★ 5147 801 DATA_RESEARCH

This repository is the official companion code for the book "Build a Reasoning Model (From Scratch)" by Sebastian Raschka (Manning, 2025). It is primarily a data_research codebase focused on building, evaluating, and training reasoning LLMs from scratch using the Qwen3 0.6B model as a base. The code covers inference-time scaling (temperature/top-p sampling, self-consistency), reinforcement learning from verifiable rewards (GRPO/RLVR), self-refinement, and knowledge distillation from larger teachers (DeepSeek-R1, Qwen3 235B). It includes a full custom Qwen3 implementation with KV cache, RoPE, and grouped-query attention, plus training scripts, notebooks, benchmark evaluations (MATH-500, MMLU), and Hugging Face export adapters. While educational in nature, it demonstrates production-quality engineering for research experiments. The repository is hybrid: it provides a reusable Python package for text generation and training, but its dominant character is data_research due to its notebook-driven, experimental, and benchmark-focused workflow.

RATING: 9.2/10 REPRO: 7.5/10 MAINT: active DRIFT: low
#reasoning-model #grpo #inference-scaling #self-consistency #distillation #math-reasoning #educational #reinforcement-learning #data-research #ai #artificial-intelligence #deep-learning #large-language-models #llms #machine-learning #python #pytorch #reasoning #chain-of-thought #inference-time-scaling #llm #reasoning-models #rlhf #test-time-compute
▸ 6 reusable components found
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