---
title: "happy-llm vs LLMs-from-scratch"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/datawhalechina-happy-llm-vs-rasbt-llms-from-scratch"
tools: ["datawhalechina-happy-llm", "rasbt-llms-from-scratch"]
---

# happy-llm vs LLMs-from-scratch

Neutral, constraint-first comparison with live GitHub stats.

| | [happy-llm](/tools/datawhalechina-happy-llm.md) | [LLMs-from-scratch](/tools/rasbt-llms-from-scratch.md) |
| --- | --- | --- |
| Tagline | 📚 从零开始构建大模型 | Implement a ChatGPT-like LLM in PyTorch from scratch, step by step |
| Stars | 31,895 | 98,748 |
| Forks | 3,024 | 15,153 |
| Open issues | 62 | 4 |
| Language | Jupyter Notebook | Jupyter Notebook |
| Adopt for | Happy-LLM 是一个系统性的 LLM 学习教程，从基础知识到动手实现大模型的全过程都有详细讲解。 | LLMs-from-scratch is a repository that offers detailed, step-by-step guidance on developing, pretraining, and finetuning GPT-like large language models using PyTorch. The codebase complements a book dedicated to building |
| Persona | developer harness | - |
| Runtime | - | - |
| License | 该项目采用其他类型许可协议，详情需查看具体条目。 | Other |
| Categories | Evaluation & Observability, Model Training | LLM Frameworks, Model Training |

## Trust and health

_Sourced signals - not a safety guarantee. No winner column._

| | [happy-llm](/tools/datawhalechina-happy-llm.md) | [LLMs-from-scratch](/tools/rasbt-llms-from-scratch.md) |
| --- | --- | --- |
| Days since push | 62d | 35d |
| Open issues (now) | 62 | 4 |
| Owner type | Organization | User |
| Security scan | No lockfile | 34 low (34 low) |
| Full report | [trust report](/tools/datawhalechina-happy-llm/trust.md) | [trust report](/tools/rasbt-llms-from-scratch/trust.md) |

**Typed relationship:** happy-llm _(alternative)_ LLMs-from-scratch

Both Happy-LLM and rasbt/llms-from-scratch offer step-by-step guides to implement large language models from scratch but may differ in their approach or level of detail.

## Decision facts: happy-llm

- **Pricing:** freemium - 完全免费的开源项目，任何人均可访问和利用其所有的学习材料。
- **Requirements:** Min 16 GB RAM; Requires Docker; - 需要一定的硬件支持（如推荐至少有16GB RAM）。; - 根据项目的README建议，使用Docker环境可以获得更好的开发和运行体验。
- **Adopt for:** Happy-LLM 是一个系统性的 LLM 学习教程，从基础知识到动手实现大模型的全过程都有详细讲解。
- **License detail:** 该项目采用其他类型许可协议，详情需查看具体条目。
- **Persona:** developer harness

## Decision facts: LLMs-from-scratch

- **Requirements:** Min 8 GB RAM; The repository includes comprehensive documentation that can be used alongside the book 'Build a Large Language Model (From Scratch)' for additional context and
- **Adopt for:** LLMs-from-scratch is a repository that offers detailed, step-by-step guidance on developing, pretraining, and finetuning GPT-like large language models using PyTorch. The codebase complements a book dedicated to building

## Choose when

### Choose happy-llm if…

- Pricing: 完全免费的开源项目，任何人均可访问和利用其所有的学习材料。.
- Requirements: Min 16 GB RAM; Requires Docker; - 需要一定的硬件支持（如推荐至少有16GB RAM）。; - 根据项目的README建议，使用Docker环境可以获得更好的开发和运行体验。.
- Both Happy-LLM and rasbt/llms-from-scratch offer step-by-step guides to implement large language models from scratch but may differ in their approach or level of detail.
- Tags unique to happy-llm: llm, rag, agent.
- Also covers Evaluation & Observability.
- - 当你需要系统学习 LLM 原理和训练过程时。

### Choose LLMs-from-scratch if…

- Requirements: Min 8 GB RAM; The repository includes comprehensive documentation that can be used alongside the book 'Build a Large Language Model (From Scratch)' for additional context and.
- Both Happy-LLM and rasbt/llms-from-scratch offer step-by-step guides to implement large language models from scratch but may differ in their approach or level of detail.
- Tags unique to LLMs-from-scratch: deep-learning, ai, artificial-intelligence, instruction-tuning.
- Also covers LLM Frameworks.
- When you need detailed, step-by-step explanations and examples for constructing an LLM from scratch with PyTorch.

## When NOT to use happy-llm

- - 如果你已经熟悉了LLM的所有基础和高级概念，此工具不会提供新的见解。
- - 非中文阅读者可能需要额外的时间去理解文档内容以及社区资源。
- - 如果目标是快速实现特定的小型模型，而无需深入了解背后的机制。

## When NOT to use LLMs-from-scratch

- When you are looking for a quick setup or already have familiarity with LLMs as the repository emphasizes building from scratch, which can be time-consuming.
- If your primary goal is production-scale deployment rather than educational understanding, as this tool focuses more on learning through thoroughness rather than speed and optimization.
- For users who prefer not to use specific frameworks like PyTorch and are interested in developing models with other libraries.

## Common questions

### What is the difference between happy-llm and LLMs-from-scratch?

happy-llm: 📚 从零开始构建大模型. 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 happy-llm over LLMs-from-scratch?

Choose happy-llm over LLMs-from-scratch when Pricing: 完全免费的开源项目，任何人均可访问和利用其所有的学习材料。; Requirements: Min 16 GB RAM; Requires Docker; - 需要一定的硬件支持（如推荐至少有16GB RAM）。; - 根据项目的README建议，使用Docker环境可以获得更好的开发和运行体验。; Both Happy-LLM and rasbt/llms-from-scratch offer step-by-step guides to implement large language models from scratch but may differ in their approach or level of detail; Tags unique to happy-llm: llm, rag, agent; Also covers Evaluation & Observability; - 当你需要系统学习 LLM 原理和训练过程时。.

### When should I choose LLMs-from-scratch over happy-llm?

Choose LLMs-from-scratch over happy-llm when Requirements: Min 8 GB RAM; The repository includes comprehensive documentation that can be used alongside the book 'Build a Large Language Model (From Scratch)' for additional context and; Both Happy-LLM and rasbt/llms-from-scratch offer step-by-step guides to implement large language models from scratch but may differ in their approach or level of detail; Tags unique to LLMs-from-scratch: deep-learning, ai, artificial-intelligence, instruction-tuning; Also covers LLM Frameworks; When you need detailed, step-by-step explanations and examples for constructing an LLM from scratch with PyTorch.

### When should I avoid happy-llm?

- 如果你已经熟悉了LLM的所有基础和高级概念，此工具不会提供新的见解。 - 非中文阅读者可能需要额外的时间去理解文档内容以及社区资源。 - 如果目标是快速实现特定的小型模型，而无需深入了解背后的机制。

### When should I avoid LLMs-from-scratch?

When you are looking for a quick setup or already have familiarity with LLMs as the repository emphasizes building from scratch, which can be time-consuming. If your primary goal is production-scale deployment rather than educational understanding, as this tool focuses more on learning through thoroughness rather than speed and optimization. For users who prefer not to use specific frameworks like PyTorch and are interested in developing models with other libraries.

### Is happy-llm or LLMs-from-scratch more popular on GitHub?

LLMs-from-scratch has more GitHub stars (98,748 vs 31,895). Stars measure visibility, not whether either tool fits your constraints.

### Are happy-llm and LLMs-from-scratch open source?

Yes - both are open-source projects on GitHub (happy-llm: Other, LLMs-from-scratch: Other).

### Where can I find alternatives to happy-llm or LLMs-from-scratch?

GraphCanon lists graph-backed alternatives at /tools/datawhalechina-happy-llm/alternatives and /tools/rasbt-llms-from-scratch/alternatives (/tools/datawhalechina-happy-llm/alternatives.md, /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 /compare/datawhalechina-happy-llm-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, happy-llm or LLMs-from-scratch?

happy-llm: Steady. 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 happy-llm and LLMs-from-scratch?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: happy-llm: /tools/datawhalechina-happy-llm/trust; LLMs-from-scratch: /tools/rasbt-llms-from-scratch/trust.

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=datawhalechina-happy-llm`](/api/graphcanon/graph?tool=datawhalechina-happy-llm)
- 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/_