Comparison
Hands-On-Large-Language-Models vs LLMs-from-scratch
Verdict
Pick Hands-On-Large-Language-Models if hands-On-Large-Language-Models offers code examples and educational content for large language models, as detailed in the O'Reilly book 'Hands-On Large Language Models'. The repository is in Jupyter Notebook format and,遵; pick LLMs-from-scratch if lLMs-from-scratch is a guide for building a ChatGPT-like language model using PyTorch, aimed at educational purposes and designed for use with accessible hardware.
Markdown twin · Hands-On-Large-Language-Models alternatives · LLMs-from-scratch alternatives
GraphCanon updated Sep 20, 2026
16views this month
vs
Trust & integrity
| Signal | Hands-On-Large-Language-Models | LLMs-from-scratch |
|---|---|---|
| Maintenance | Slowing (146d since push) As of Sep 18, 2026 · github_public_v1 | Very active (0d since push) As of Sep 18, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 18, 2026 · github_public_v1 | Not a fork · Personal account As of Sep 18, 2026 · github_public_v1 |
| OSV dependency advisories | Published findings As of Sep 18, 2026 · osv@v1 | Published findings As of Sep 18, 2026 · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- Hands-On-Large-Language-Models
- Code for the book 'Hands-On Large Language Models'
- LLMs-from-scratch
- Implement a ChatGPT-like LLM in PyTorch from scratch, step by step
Stars
- Hands-On-Large-Language-Models
- 29k
- LLMs-from-scratch
- 105k
Forks
- Hands-On-Large-Language-Models
- 6.7k
- LLMs-from-scratch
- 16k
Open issues
- Hands-On-Large-Language-Models
- 40
- LLMs-from-scratch
- 2
Language
- Hands-On-Large-Language-Models
- Jupyter Notebook
- LLMs-from-scratch
- Jupyter Notebook
Adopt for
- Hands-On-Large-Language-Models
- Hands-On-Large-Language-Models offers code examples and educational content for large language models, as detailed in the O'Reilly book 'Hands-On Large Language Models'. The repository is in Jupyter Notebook format and,遵
- LLMs-from-scratch
- LLMs-from-scratch is a guide for building a ChatGPT-like language model using PyTorch, aimed at educational purposes and designed for use with accessible hardware.
Persona
- Hands-On-Large-Language-Models
- -
- LLMs-from-scratch
- -
Runtime
- Hands-On-Large-Language-Models
- -
- LLMs-from-scratch
- -
License
- Hands-On-Large-Language-Models
- Apache-2.0
- LLMs-from-scratch
- The license is not specified as a standard open-source license, indicating that users should review the license terms carefully before use.
Last pushed
- Hands-On-Large-Language-Models
- Apr 24, 2026
- LLMs-from-scratch
- Sep 17, 2026
Categories
- Hands-On-Large-Language-Models
- Developer Tools, LLM Frameworks
- LLMs-from-scratch
- LLM Frameworks, Model Training
Trust and health
Maintenance
- Hands-On-Large-Language-Models
- Slowing (36%)
- LLMs-from-scratch
- Very active (96%)
Days since push
- Hands-On-Large-Language-Models
- 146d
- LLMs-from-scratch
- 0d
Open issues (now)
- Hands-On-Large-Language-Models
- 40
- LLMs-from-scratch
- 2
Stars delta
- Hands-On-Large-Language-Models
- +910 (30d)
- LLMs-from-scratch
- +2.4k (30d)
Open issues delta
- Hands-On-Large-Language-Models
- +2 (30d)
- LLMs-from-scratch
- 0 (30d)
Owner type
- Hands-On-Large-Language-Models
- Organization
- LLMs-from-scratch
- User
Full report
- Hands-On-Large-Language-Models
- Trust report
- LLMs-from-scratch
- Trust report
Typed relationship
Hands-On-Large-Language-Models alternative LLMs-from-scratchBoth repositories are focused on hands-on tutorials and implementation of large language models, although 'Hands-On-Large-Language-Models' might provide a broader range of content as it is associated with an O'Reilly book.
Choose Hands-On-Large-Language-Models if…
- License: Hands-On-Large-Language-Models is Apache-2.0, LLMs-from-scratch is Other.
- Both repositories are focused on hands-on tutorials and implementation of large language models, although 'Hands-On-Large-Language-Models' might provide a broader range of content as it is associated with an O'Reilly book.
- Tags unique to Hands-On-Large-Language-Models: book, large-language-models, llm, llms.
- Also covers Developer Tools.
- Use this tool if you are looking for practical, code-based learning resources that align with the book 'Hands-On Large Language Models'.
When NOT to use Hands-On-Large-Language-Models
- Avoid this tool if you are seeking a comprehensive theoretical background without practical examples, as the focus is on hands-on learning.
- Not recommended for users who prefer a different platform for running code examples, as the repository is optimized for Google Colab with a T4 GPU and 16GB VRAM.
- If you are looking for a tool that covers a broader range of AI topics beyond large language models, this repository may not be the best fit.
Choose LLMs-from-scratch if…
- License: LLMs-from-scratch is Other, Hands-On-Large-Language-Models 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 are focused on hands-on tutorials and implementation of large language models, although 'Hands-On-Large-Language-Models' might provide a broader range of content as it is associated with an O'Reilly book.
- Tags unique to LLMs-from-scratch: ai, attention-mechanism, deep-learning, finetuning.
- Also covers Model Training.
- When you are interested in understanding the inner workings of large language models and want to build one from scratch using PyTorch.
When NOT to use 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (HandsOnLLM/Hands-On-Large-Language-Models) · observed Sep 20, 2026
- GitHub forks (HandsOnLLM/Hands-On-Large-Language-Models) · observed Sep 20, 2026
- Last push (HandsOnLLM/Hands-On-Large-Language-Models) · observed Apr 24, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Sep 18, 2026
- Trust scan (lockfile / OSV) · observed Sep 18, 2026
- GitHub stars (rasbt/LLMs-from-scratch) · observed Sep 20, 2026
- GitHub forks (rasbt/LLMs-from-scratch) · observed Sep 20, 2026
- Last push (rasbt/LLMs-from-scratch) · observed Sep 17, 2026
- License file (Other) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Sep 18, 2026
- Trust scan (lockfile / OSV) · observed Sep 18, 2026
GitHub stars on cards: Hands-On-Large-Language-Models 29k · LLMs-from-scratch 105k (synced Sep 20, 2026).
Common questions
- What is the difference between Hands-On-Large-Language-Models and LLMs-from-scratch?
- Hands-On-Large-Language-Models: Code for the book 'Hands-On Large Language Models'. 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 Hands-On-Large-Language-Models over LLMs-from-scratch?
- Choose Hands-On-Large-Language-Models over LLMs-from-scratch when License: Hands-On-Large-Language-Models is Apache-2.0, LLMs-from-scratch is Other; Both repositories are focused on hands-on tutorials and implementation of large language models, although 'Hands-On-Large-Language-Models' might provide a broader range of content as it is associated with an O'Reilly book; Tags unique to Hands-On-Large-Language-Models: book, large-language-models, llm, llms; Also covers Developer Tools; Use this tool if you are looking for practical, code-based learning resources that align with the book 'Hands-On Large Language Models'.
- When should I choose LLMs-from-scratch over Hands-On-Large-Language-Models?
- Choose LLMs-from-scratch over Hands-On-Large-Language-Models when License: LLMs-from-scratch is Other, Hands-On-Large-Language-Models 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 are focused on hands-on tutorials and implementation of large language models, although 'Hands-On-Large-Language-Models' might provide a broader range of content as it is associated with an O'Reilly book; Tags unique to LLMs-from-scratch: ai, attention-mechanism, deep-learning, finetuning; Also covers Model Training; 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 avoid Hands-On-Large-Language-Models?
- Avoid this tool if you are seeking a comprehensive theoretical background without practical examples, as the focus is on hands-on learning. Not recommended for users who prefer a different platform for running code examples, as the repository is optimized for Google Colab with a T4 GPU and 16GB VRAM. If you are looking for a tool that covers a broader range of AI topics beyond large language models, this repository may not be the best fit.
- 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.
- Is Hands-On-Large-Language-Models or LLMs-from-scratch more popular on GitHub?
- LLMs-from-scratch has more GitHub stars (105,176 vs 29,162). Stars measure visibility, not whether either tool fits your constraints.
- Are Hands-On-Large-Language-Models and LLMs-from-scratch open source?
- Yes - both are open-source projects on GitHub (Hands-On-Large-Language-Models: Apache-2.0, LLMs-from-scratch: Other).
- Where can I find alternatives to Hands-On-Large-Language-Models or LLMs-from-scratch?
- GraphCanon lists graph-backed alternatives at Hands-On-Large-Language-Models alternatives and LLMs-from-scratch alternatives (Hands-On-Large-Language-Models 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, Hands-On-Large-Language-Models or LLMs-from-scratch?
- Hands-On-Large-Language-Models: Slowing. LLMs-from-scratch: 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 LLMs-from-scratch?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Hands-On-Large-Language-Models trust report; LLMs-from-scratch trust report.