LLMs-from-scratch vs reasoning-from-scratch

Common questions

What is the difference between LLMs-from-scratch and reasoning-from-scratch?

LLMs-from-scratch: Implement a ChatGPT-like LLM in PyTorch from scratch, step by step. reasoning-from-scratch: Implement a reasoning LLM in PyTorch from scratch, step by step. See the comparison table for live GitHub stats and shared categories.

When should I choose LLMs-from-scratch over reasoning-from-scratch?

Choose LLMs-from-scratch over reasoning-from-scratch when License: LLMs-from-scratch is Other, reasoning-from-scratch is Apache-2.0; Requirements: Min 8 GB RAM; The code automatically utilizes GPUs if they are available, but it is designed to run on conventional laptops without specialized hardware.; Both repositories aim to implement a large language model from scratch in PyTorch, but they likely differ slightly in implementation and specifics; Tags unique to LLMs-from-scratch: attention-mechanism, finetuning, generative-ai, gpt; When you are interested in understanding the inner workings of large language models and want to build one from scratch using PyTorch.

When should I choose reasoning-from-scratch over LLMs-from-scratch?

Choose reasoning-from-scratch over LLMs-from-scratch when License: reasoning-from-scratch is Apache-2.0, LLMs-from-scratch is Other; Requirements: Automatic GPU utilization where available, though not strictly necessary for the early chapters.; Both repositories aim to implement a large language model from scratch in PyTorch, but they likely differ slightly in implementation and specifics; Tags unique to reasoning-from-scratch: chain-of-thought, distillation, inference-time-scaling, large-language-models; When you have intermediate knowledge of PyTorch and want detailed insights into the implementation process of reasoning LLMS.

When should I avoid LLMs-from-scratch?

If you are looking for a quick and efficient way to deploy a pre-trained language model without delving into the implementation details. When you require a production-ready solution, as LLMs-from-scratch is primarily designed for educational purposes and may not be optimized for industrial use.

When should I avoid reasoning-from-scratch?

Avoid this tool if you are looking for rapid prototyping or quick model deployment; it focuses more on understanding and building the LLM from scratch rather than providing prebuilt components. If specialized server hardware is available and preferred for the entire project, as chapters 5 and 6 recommend GPU use but earlier sections can be completed with just a CPU.

Is LLMs-from-scratch or reasoning-from-scratch more popular on GitHub?

LLMs-from-scratch has more GitHub stars (105,176 vs 4,998). Stars measure visibility, not whether either tool fits your constraints.

Are LLMs-from-scratch and reasoning-from-scratch open source?

Yes - both are open-source projects on GitHub (LLMs-from-scratch: Other, reasoning-from-scratch: Apache-2.0).

Where can I find alternatives to LLMs-from-scratch or reasoning-from-scratch?

GraphCanon lists graph-backed alternatives at LLMs-from-scratch alternatives and reasoning-from-scratch alternatives (LLMs-from-scratch markdown twin, reasoning-from-scratch markdown twin), ranked by typed relationship edges rather than popularity votes.

Is there a machine-readable version of this comparison?

Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

Which is better maintained, LLMs-from-scratch or reasoning-from-scratch?

LLMs-from-scratch: Very active. reasoning-from-scratch: Active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.

Where are the full trust reports for LLMs-from-scratch and reasoning-from-scratch?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: LLMs-from-scratch trust report; reasoning-from-scratch trust report.