---
title: "Hands-On-Large-Language-Models vs awesome-LLM-resources"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/handsonllm-hands-on-large-language-models-vs-wangrongsheng-awesome-llm-resources"
tools: ["handsonllm-hands-on-large-language-models", "wangrongsheng-awesome-llm-resources"]
---

# Hands-On-Large-Language-Models vs awesome-LLM-resources

Neutral, constraint-first comparison with live GitHub stats.

| | [Hands-On-Large-Language-Models](/tools/handsonllm-hands-on-large-language-models.md) | [awesome-LLM-resources](/tools/wangrongsheng-awesome-llm-resources.md) |
| --- | --- | --- |
| Tagline | Official code repo for the O'Reilly Book - 'Hands-On Large Language Models' | Summary of the world's best LLM resources |
| Stars | 27,427 | 8,658 |
| Forks | 6,389 | 921 |
| Open issues | 37 | 40 |
| Language | Jupyter Notebook | - |
| Adopt for | The 'Hands-On Large Language Models' repository, backed by Jay Alammar and Maarten Grootendorst, is a comprehensive collection of code examples from their book on large language models. It's designed to simplify the use, | awesome-LLM-resources 是一个汇集全球范围内最优质的语言模型资源的开源项目，提供从多模态生成到小语言模型的各种内容。 |
| Persona | - | - |
| Runtime | - | - |
| License | Apache-2.0 | Apache-2.0 |
| Categories | LLM Frameworks, Developer Tools | AI Agents, Evaluation & Observability, Data & Retrieval, LLM Frameworks, Model Training, Inference & Serving, Speech & Audio, Computer Vision |

## Trust and health

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

| | [Hands-On-Large-Language-Models](/tools/handsonllm-hands-on-large-language-models.md) | [awesome-LLM-resources](/tools/wangrongsheng-awesome-llm-resources.md) |
| --- | --- | --- |
| Maintenance | Steady (60%) | Very active (96%) |
| Days since push | 75d | 0d |
| Open issues (now) | 37 | 40 |
| Owner type | Organization | User |
| Security scan | 96 low (96 low) | No lockfile |
| Full report | [trust report](/tools/handsonllm-hands-on-large-language-models/trust.md) | [trust report](/tools/wangrongsheng-awesome-llm-resources/trust.md) |

**Typed relationship:** Hands-On-Large-Language-Models _(alternative)_ awesome-LLM-resources

Both compile comprehensive sets of LLM-related resources, though with slightly different focuses.

## Decision facts: Hands-On-Large-Language-Models

- **Adopt for:** The 'Hands-On Large Language Models' repository, backed by Jay Alammar and Maarten Grootendorst, is a comprehensive collection of code examples from their book on large language models. It's designed to simplify the use,

## Decision facts: awesome-LLM-resources

- **Adopt for:** awesome-LLM-resources 是一个汇集全球范围内最优质的语言模型资源的开源项目，提供从多模态生成到小语言模型的各种内容。

## Choose when

### Choose Hands-On-Large-Language-Models if…

- Both compile comprehensive sets of LLM-related resources, though with slightly different focuses.
- Tags unique to Hands-On-Large-Language-Models: artificial-intelligence.
- Also covers Developer Tools.
- When you seek practical insights into LLMs complemented with nearly 300 custom-made figures for educational clarity;

### Choose awesome-LLM-resources if…

- Both compile comprehensive sets of LLM-related resources, though with slightly different focuses.
- Tags unique to awesome-LLM-resources: llama, mistral, llm, course.
- Also covers AI Agents, Evaluation & Observability, Data & Retrieval, Model Training, Inference & Serving, Speech & Audio, Computer Vision.
- - 当你需要综合各种LLM相关工具与资料时

## When NOT to use Hands-On-Large-Language-Models

- If your workflow does not include hands-on coding within Jupyter Notebooks and you do not require the visual educational elements provided by custom figures.
- When you need support or solutions using platforms other than Google Colab as setup examples and stability assurances are specifically tailored for Google Colab.
- If advanced theoretical insights beyond practical usage of LLMs are your priority, since this tool focuses more on hands-on application rather than deep theory.
- In scenarios where immediate access to the latest technical support from a wide community is essential, as this repository’s community might be more niche compared to broader, more generic developer L

## When NOT to use awesome-LLM-resources

- - 如果你需要一个专注于极具体专项任务（例如特定的数据集合分析）的工具，此项目可能提供的是概述而不是深入指南。
- - 对于需要高度定制化需求的企业或个人如果项目中没有涵盖到你关注的具体细分领域细节，awesome-LLM-resources 可能不是最佳选择。
- - 当你需要一个具有交互界面的资源库来快速实验特定技术时, 因为 awesome-LLM-resources 主要是以列表形式整理资源
- - 如果您主要是寻找最新的商业产品或服务而不是开源项目的话，该资源可能不那么适用

## Common questions

### What is the difference between Hands-On-Large-Language-Models and awesome-LLM-resources?

Hands-On-Large-Language-Models: Official code repo for the O'Reilly Book - 'Hands-On Large Language Models'. awesome-LLM-resources: Summary of the world's best LLM resources. See the comparison table for live GitHub stats and shared categories.

### When should I choose Hands-On-Large-Language-Models over awesome-LLM-resources?

Choose Hands-On-Large-Language-Models over awesome-LLM-resources when Both compile comprehensive sets of LLM-related resources, though with slightly different focuses; Tags unique to Hands-On-Large-Language-Models: artificial-intelligence; Also covers Developer Tools; When you seek practical insights into LLMs complemented with nearly 300 custom-made figures for educational clarity;.

### When should I choose awesome-LLM-resources over Hands-On-Large-Language-Models?

Choose awesome-LLM-resources over Hands-On-Large-Language-Models when Both compile comprehensive sets of LLM-related resources, though with slightly different focuses; Tags unique to awesome-LLM-resources: llama, mistral, llm, course; Also covers AI Agents, Evaluation & Observability, Data & Retrieval, Model Training, Inference & Serving, Speech & Audio, Computer Vision; - 当你需要综合各种LLM相关工具与资料时.

### When should I avoid Hands-On-Large-Language-Models?

If your workflow does not include hands-on coding within Jupyter Notebooks and you do not require the visual educational elements provided by custom figures. When you need support or solutions using platforms other than Google Colab as setup examples and stability assurances are specifically tailored for Google Colab. If advanced theoretical insights beyond practical usage of LLMs are your priority, since this tool focuses more on hands-on application rather than deep theory. In scenarios where immediate access to the latest technical support from a wide community is essential, as this repository’s community might be more niche compared to broader, more generic developer L

### When should I avoid awesome-LLM-resources?

- 如果你需要一个专注于极具体专项任务（例如特定的数据集合分析）的工具，此项目可能提供的是概述而不是深入指南。 - 对于需要高度定制化需求的企业或个人如果项目中没有涵盖到你关注的具体细分领域细节，awesome-LLM-resources 可能不是最佳选择。 - 当你需要一个具有交互界面的资源库来快速实验特定技术时, 因为 awesome-LLM-resources 主要是以列表形式整理资源 - 如果您主要是寻找最新的商业产品或服务而不是开源项目的话，该资源可能不那么适用

### Is Hands-On-Large-Language-Models or awesome-LLM-resources more popular on GitHub?

Hands-On-Large-Language-Models has more GitHub stars (27,427 vs 8,658). Stars measure visibility, not whether either tool fits your constraints.

### Are Hands-On-Large-Language-Models and awesome-LLM-resources open source?

Yes - both are open-source projects on GitHub (Hands-On-Large-Language-Models: Apache-2.0, awesome-LLM-resources: Apache-2.0).

### Where can I find alternatives to Hands-On-Large-Language-Models or awesome-LLM-resources?

GraphCanon lists graph-backed alternatives at /tools/handsonllm-hands-on-large-language-models/alternatives and /tools/wangrongsheng-awesome-llm-resources/alternatives (/tools/handsonllm-hands-on-large-language-models/alternatives.md, /tools/wangrongsheng-awesome-llm-resources/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/handsonllm-hands-on-large-language-models-vs-wangrongsheng-awesome-llm-resources.md mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, Hands-On-Large-Language-Models or awesome-LLM-resources?

Hands-On-Large-Language-Models: Steady. awesome-LLM-resources: Very 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 Hands-On-Large-Language-Models and awesome-LLM-resources?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Hands-On-Large-Language-Models: /tools/handsonllm-hands-on-large-language-models/trust; awesome-LLM-resources: /tools/wangrongsheng-awesome-llm-resources/trust.

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=handsonllm-hands-on-large-language-models`](/api/graphcanon/graph?tool=handsonllm-hands-on-large-language-models)
- 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/_