human-eval vs LLMs-from-scratch

Common questions

What is the difference between human-eval and LLMs-from-scratch?

human-eval: Code for the paper "Evaluating Large Language Models Trained on Code". LLMs-from-scratch: Implement a ChatGPT-like LLM in PyTorch from scratch, step by step. See the comparison table for live GitHub stats and shared categories.

When should I choose human-eval over LLMs-from-scratch?

Choose human-eval over LLMs-from-scratch when human-eval is primarily Python; LLMs-from-scratch is Jupyter Notebook; License: human-eval is MIT, LLMs-from-scratch is Other; Tags unique to human-eval: python; Also covers Evaluation & Observability.

When should I choose LLMs-from-scratch over human-eval?

Choose LLMs-from-scratch over human-eval when LLMs-from-scratch is primarily Jupyter Notebook; human-eval is Python; License: LLMs-from-scratch is Other, human-eval is MIT; Tags unique to LLMs-from-scratch: deep-learning, ai, artificial-intelligence, attention-mechanism; - You are an advanced practitioner aiming to fully understand the underpinnings of LLMs using PyTorch as your primary framework.

When should I avoid human-eval?

Last GitHub push was 540 days ago (dormant maintenance, Jan 17, 2025). Validate activity before betting a new project on human-eval. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers.

When should I avoid LLMs-from-scratch?

- If you are looking for a rapid deployment of an LLM without understanding its intricate structure - this tool requires extensive manual and conceptual work. - You prefer frameworks with automatic model generation or other high-level abstractions that simplify the process. This repository emphasizes manual creation, which is more time-consuming but offers丰 a deeper learning experience.

Is human-eval or LLMs-from-scratch more popular on GitHub?

LLMs-from-scratch has more GitHub stars (98,899 vs 3,294). Stars measure visibility, not whether either tool fits your constraints.

Are human-eval and LLMs-from-scratch open source?

Yes - both are open-source projects on GitHub (human-eval: MIT, LLMs-from-scratch: Other).

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

GraphCanon lists graph-backed alternatives at human-eval alternatives and LLMs-from-scratch alternatives (human-eval markdown twin, LLMs-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, human-eval or LLMs-from-scratch?

human-eval: Dormant. LLMs-from-scratch: Steady. 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 human-eval and LLMs-from-scratch?

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