Comparison
LLMs-from-scratch vs Awesome-AI-Data-Guided-Projects
Verdict
Pick LLMs-from-scratch when license: LLMs-from-scratch is Other, Awesome-AI-Data-Guided-Projects is GPL-3.0; pick Awesome-AI-Data-Guided-Projects when license: Awesome-AI-Data-Guided-Projects is GPL-3.0, LLMs-from-scratch is Other.
Markdown twin · LLMs-from-scratch alternatives · Awesome-AI-Data-Guided-Projects alternatives
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Trust & integrity
| Signal | LLMs-from-scratch | Awesome-AI-Data-Guided-Projects |
|---|---|---|
| Maintenance | Steady (38d since push) As of today · github_public_v1 | Dormant (797d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Personal account As of today · github_public_v1 | Not a fork · Personal account As of today · github_public_v1 |
| Security (OSV) | No lockfile As of today · none | No lockfile As of today · none |
Tagline
- LLMs-from-scratch
- Implement a ChatGPT-like LLM in PyTorch from scratch, step by step
- Awesome-AI-Data-Guided-Projects
- A curated list of data science & AI guided projects to start building your portfolio
Stars
- LLMs-from-scratch
- 99k
- Awesome-AI-Data-Guided-Projects
- 722
Forks
- LLMs-from-scratch
- 15k
- Awesome-AI-Data-Guided-Projects
- 150
Open issues
- LLMs-from-scratch
- 4
- Awesome-AI-Data-Guided-Projects
- 2
Language
- LLMs-from-scratch
- Jupyter Notebook
- Awesome-AI-Data-Guided-Projects
- -
Adopt for
- LLMs-from-scratch
- 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.
- Awesome-AI-Data-Guided-Projects
- -
Persona
- LLMs-from-scratch
- -
- Awesome-AI-Data-Guided-Projects
- -
Runtime
- LLMs-from-scratch
- -
- Awesome-AI-Data-Guided-Projects
- -
License
- LLMs-from-scratch
- Other
- Awesome-AI-Data-Guided-Projects
- GPL-3.0
Last pushed
- LLMs-from-scratch
- Jun 2, 2026
- Awesome-AI-Data-Guided-Projects
- May 5, 2024
Categories
- LLMs-from-scratch
- Model Training, LLM Frameworks
- Awesome-AI-Data-Guided-Projects
- Model Training, LLM Frameworks, Inference & Serving
Trust and health
Maintenance
- LLMs-from-scratch
- Steady (60%)
- Awesome-AI-Data-Guided-Projects
- Dormant (18%)
Days since push
- LLMs-from-scratch
- 38d
- Awesome-AI-Data-Guided-Projects
- 797d
Open issues (now)
- LLMs-from-scratch
- 4
- Awesome-AI-Data-Guided-Projects
- 2
Full report
- LLMs-from-scratch
- Trust report
- Awesome-AI-Data-Guided-Projects
- Trust report
Choose LLMs-from-scratch if…
- License: LLMs-from-scratch is Other, Awesome-AI-Data-Guided-Projects is GPL-3.0.
- Tags unique to LLMs-from-scratch: artificial-intelligence, attention-mechanism, from-scratch, generative-ai.
- - You are an advanced practitioner aiming to fully understand the underpinnings of LLMs using PyTorch as your primary framework.
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丰
- a deeper learning experience.
Choose Awesome-AI-Data-Guided-Projects if…
- License: Awesome-AI-Data-Guided-Projects is GPL-3.0, LLMs-from-scratch is Other.
- Tags unique to Awesome-AI-Data-Guided-Projects: llm, datascience, machine-learning, computer-vision.
- Also covers Inference & Serving.
When NOT to use Awesome-AI-Data-Guided-Projects
- Last GitHub push was 797 days ago (dormant maintenance, May 5, 2024). Validate activity before betting a new project on Awesome-AI-Data-Guided-Projects.
- Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
- Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (rasbt/LLMs-from-scratch) · observed Jul 11, 2026
- GitHub forks (rasbt/LLMs-from-scratch) · observed Jul 11, 2026
- Last push (rasbt/LLMs-from-scratch) · observed Jun 2, 2026
- License file (Other) · observed Jul 11, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (youssefHosni/Awesome-AI-Data-Guided-Projects) · observed Jul 11, 2026
- GitHub forks (youssefHosni/Awesome-AI-Data-Guided-Projects) · observed Jul 11, 2026
- Last push (youssefHosni/Awesome-AI-Data-Guided-Projects) · observed May 5, 2024
- License file (GPL-3.0) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: LLMs-from-scratch 99k · Awesome-AI-Data-Guided-Projects 722 (synced Jul 11, 2026).
Common questions
- What is the difference between LLMs-from-scratch and Awesome-AI-Data-Guided-Projects?
- LLMs-from-scratch: Implement a ChatGPT-like LLM in PyTorch from scratch, step by step. Awesome-AI-Data-Guided-Projects: A curated list of data science & AI guided projects to start building your portfolio. See the comparison table for live GitHub stats and shared categories.
- When should I choose LLMs-from-scratch over Awesome-AI-Data-Guided-Projects?
- Choose LLMs-from-scratch over Awesome-AI-Data-Guided-Projects when License: LLMs-from-scratch is Other, Awesome-AI-Data-Guided-Projects is GPL-3.0; Tags unique to LLMs-from-scratch: artificial-intelligence, attention-mechanism, from-scratch, generative-ai; - You are an advanced practitioner aiming to fully understand the underpinnings of LLMs using PyTorch as your primary framework.
- When should I choose Awesome-AI-Data-Guided-Projects over LLMs-from-scratch?
- Choose Awesome-AI-Data-Guided-Projects over LLMs-from-scratch when License: Awesome-AI-Data-Guided-Projects is GPL-3.0, LLMs-from-scratch is Other; Tags unique to Awesome-AI-Data-Guided-Projects: llm, datascience, machine-learning, computer-vision; Also covers Inference & Serving.
- 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.
- When should I avoid Awesome-AI-Data-Guided-Projects?
- Last GitHub push was 797 days ago (dormant maintenance, May 5, 2024). Validate activity before betting a new project on Awesome-AI-Data-Guided-Projects. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic.
- Is LLMs-from-scratch or Awesome-AI-Data-Guided-Projects more popular on GitHub?
- LLMs-from-scratch has more GitHub stars (98,899 vs 722). Stars measure visibility, not whether either tool fits your constraints.
- Are LLMs-from-scratch and Awesome-AI-Data-Guided-Projects open source?
- Yes - both are open-source projects on GitHub (LLMs-from-scratch: Other, Awesome-AI-Data-Guided-Projects: GPL-3.0).
- Where can I find alternatives to LLMs-from-scratch or Awesome-AI-Data-Guided-Projects?
- GraphCanon lists graph-backed alternatives at LLMs-from-scratch alternatives and Awesome-AI-Data-Guided-Projects alternatives (LLMs-from-scratch markdown twin, Awesome-AI-Data-Guided-Projects 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, LLMs-from-scratch or Awesome-AI-Data-Guided-Projects?
- LLMs-from-scratch: Steady. Awesome-AI-Data-Guided-Projects: Dormant. 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 LLMs-from-scratch and Awesome-AI-Data-Guided-Projects?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: LLMs-from-scratch trust report; Awesome-AI-Data-Guided-Projects trust report.