--- title: "PHUDGE vs LLMs-from-scratch" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/deshwalmahesh-phudge-vs-rasbt-llms-from-scratch" tools: ["deshwalmahesh-phudge", "rasbt-llms-from-scratch"] --- # PHUDGE vs LLMs-from-scratch *GraphCanon updated Jul 15, 2026* ## Verdict Pick PHUDGE when tags unique to PHUDGE: custom-dataset, evaluation, feedback-collection, hallucination; pick LLMs-from-scratch when tags unique to LLMs-from-scratch: artificial-intelligence, attention-mechanism, deep-learning, from-scratch. [PHUDGE](https://arxiv.org/abs/2405.08029) reports 53 GitHub stars, 7 forks, and 1 open issues, last pushed Jul 10, 2024. [LLMs-from-scratch](https://amzn.to/4fqvn0D) has 99k stars, 15k forks, and 4 open issues, last pushed Jun 2, 2026. Figures are from public GitHub metadata via [PHUDGE's repository](https://github.com/deshwalmahesh/PHUDGE) and [LLMs-from-scratch's repository](https://github.com/rasbt/LLMs-from-scratch). | | [PHUDGE](/tools/deshwalmahesh-phudge.md) | [LLMs-from-scratch](/tools/rasbt-llms-from-scratch.md) | | --- | --- | --- | | Tagline | Official repo for the paper PHUDGE: Phi-3 as Scalable Judge. Evaluate your LLMs with or without custom rubric, reference answer, absolute, relative and much more. It contains a list of all the availab | Implement a ChatGPT-like LLM in PyTorch from scratch, step by step | | Stars | 53 | 98,899 | | Forks | 7 | 15,183 | | Open issues | 1 | 4 | | Language | Jupyter Notebook | Jupyter Notebook | | Adopt for | - | LLMs-from-scratch is a project-oriented repository aimed at building PyTorch-based language models from the ground up, with detailed step-by-step instructions. | | Persona | - | - | | Runtime | - | - | | License | - | Other | | Categories | Inference & Serving, LLM Frameworks, Model Training | LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [PHUDGE](/tools/deshwalmahesh-phudge.md) | [LLMs-from-scratch](/tools/rasbt-llms-from-scratch.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Steady (60%) | | Days since push | 734d | 38d | | Open issues (now) | 1 | 4 | | Full report | [trust report](/tools/deshwalmahesh-phudge/trust.md) | [trust report](/tools/rasbt-llms-from-scratch/trust.md) | ## Decision facts: LLMs-from-scratch - **Adopt for:** LLMs-from-scratch is a project-oriented repository aimed at building PyTorch-based language models from the ground up, with detailed step-by-step instructions. ## Choose when ### Choose PHUDGE if… - Tags unique to PHUDGE: custom-dataset, evaluation, feedback-collection, hallucination. - Also covers Inference & Serving. - Leaner open-issue backlog (1). ### Choose LLMs-from-scratch if… - Tags unique to LLMs-from-scratch: artificial-intelligence, attention-mechanism, deep-learning, from-scratch. - - You are an advanced practitioner aiming to fully understand the underpinnings of LLMs using PyTorch as your primary framework. - More GitHub stars (99k vs 53) - visibility, not fit. ## When NOT to use PHUDGE - Last GitHub push was 734 days ago (dormant maintenance, Jul 10, 2024). Validate activity before betting a new project on PHUDGE. - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. - 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. ## When NOT to use 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 - a deeper learning experience. ## Common questions ### What is the difference between PHUDGE and LLMs-from-scratch? PHUDGE: Official repo for the paper PHUDGE: Phi-3 as Scalable Judge. Evaluate your LLMs with or without custom rubric, reference answer, absolute, relative and much more. It contains a list of all the availab. 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 PHUDGE over LLMs-from-scratch? Choose PHUDGE over LLMs-from-scratch when Tags unique to PHUDGE: custom-dataset, evaluation, feedback-collection, hallucination; Also covers Inference & Serving; Leaner open-issue backlog (1). ### When should I choose LLMs-from-scratch over PHUDGE? Choose LLMs-from-scratch over PHUDGE when Tags unique to LLMs-from-scratch: artificial-intelligence, attention-mechanism, deep-learning, from-scratch; - You are an advanced practitioner aiming to fully understand the underpinnings of LLMs using PyTorch as your primary framework; More GitHub stars (99k vs 53) - visibility, not fit. ### When should I avoid PHUDGE? Last GitHub push was 734 days ago (dormant maintenance, Jul 10, 2024). Validate activity before betting a new project on PHUDGE. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. 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. ### 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 a deeper learning experience. ### Is PHUDGE or LLMs-from-scratch more popular on GitHub? LLMs-from-scratch has more GitHub stars (98,899 vs 53). Stars measure visibility, not whether either tool fits your constraints. ### Are PHUDGE and LLMs-from-scratch open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to PHUDGE or LLMs-from-scratch? GraphCanon lists graph-backed alternatives at [PHUDGE alternatives](/tools/deshwalmahesh-phudge/alternatives) and [LLMs-from-scratch alternatives](/tools/rasbt-llms-from-scratch/alternatives) ([PHUDGE markdown twin](/tools/deshwalmahesh-phudge/alternatives.md), [LLMs-from-scratch markdown twin](/tools/rasbt-llms-from-scratch/alternatives.md)), 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](/compare/deshwalmahesh-phudge-vs-rasbt-llms-from-scratch.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, PHUDGE or LLMs-from-scratch? PHUDGE: 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 PHUDGE and LLMs-from-scratch? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [PHUDGE trust report](/tools/deshwalmahesh-phudge/trust); [LLMs-from-scratch trust report](/tools/rasbt-llms-from-scratch/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=deshwalmahesh-phudge`](/api/graphcanon/graph?tool=deshwalmahesh-phudge) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_