Comparison
LLMs-from-scratch vs PROMPTPurify
Verdict
Pick LLMs-from-scratch when lLMs-from-scratch is primarily Jupyter Notebook; PROMPTPurify is TypeScript; pick PROMPTPurify when pROMPTPurify is primarily TypeScript; LLMs-from-scratch is Jupyter Notebook.
Markdown twin · LLMs-from-scratch alternatives · PROMPTPurify alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | LLMs-from-scratch | PROMPTPurify |
|---|---|---|
| Maintenance | Steady (38d since push) As of 4d · github_public_v1 | Steady (44d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Personal account As of 4d · github_public_v1 | Not a fork · Organization account As of today · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 4d · osv@v1 | Published findings As of today · 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
- LLMs-from-scratch
- Implement a ChatGPT-like LLM in PyTorch from scratch, step by step
- PROMPTPurify
- Prompt-injection guardrail for LLM applications. Compact model that outperforms larger open-source guards. No regex, no signatures. Demo: anton.securelayer7.net
Stars
- LLMs-from-scratch
- 99k
- PROMPTPurify
- 76
Forks
- LLMs-from-scratch
- 15k
- PROMPTPurify
- 20
Open issues
- LLMs-from-scratch
- 4
- PROMPTPurify
- 0
Language
- LLMs-from-scratch
- Jupyter Notebook
- PROMPTPurify
- TypeScript
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.
- PROMPTPurify
- -
Persona
- LLMs-from-scratch
- -
- PROMPTPurify
- -
Runtime
- LLMs-from-scratch
- -
- PROMPTPurify
- -
License
- LLMs-from-scratch
- Other
- PROMPTPurify
- MIT
Last pushed
- LLMs-from-scratch
- Jun 2, 2026
- PROMPTPurify
- May 31, 2026
Categories
- LLMs-from-scratch
- LLM Frameworks, Model Training
- PROMPTPurify
- Computer Vision, LLM Frameworks, Model Training
Trust and health
Days since push
- LLMs-from-scratch
- 38d
- PROMPTPurify
- 44d
Open issues (now)
- LLMs-from-scratch
- 4
- PROMPTPurify
- 0
Owner type
- LLMs-from-scratch
- User
- PROMPTPurify
- Organization
OSV dependency advisories
- LLMs-from-scratch
- No lockfile (source not queried)
- PROMPTPurify
- Published findings
Full report
- LLMs-from-scratch
- Trust report
- PROMPTPurify
- Trust report
Choose LLMs-from-scratch if…
- LLMs-from-scratch is primarily Jupyter Notebook; PROMPTPurify is TypeScript.
- License: LLMs-from-scratch is Other, PROMPTPurify is MIT.
- Tags unique to LLMs-from-scratch: ai, artificial-intelligence, attention-mechanism, deep-learning.
- - 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
- a deeper learning experience.
Choose PROMPTPurify if…
- PROMPTPurify is primarily TypeScript; LLMs-from-scratch is Jupyter Notebook.
- License: PROMPTPurify is MIT, LLMs-from-scratch is Other.
- Tags unique to PROMPTPurify: ai-firewall, ai-safety, ai-security, application-security.
- Also covers Computer Vision.
When NOT to use PROMPTPurify
- 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.
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 (securelayer7/PROMPTPurify) · observed Jul 15, 2026
- GitHub forks (securelayer7/PROMPTPurify) · observed Jul 15, 2026
- Last push (securelayer7/PROMPTPurify) · observed May 31, 2026
- License file (MIT) · observed Jul 15, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
GitHub stars on cards: LLMs-from-scratch 99k · PROMPTPurify 76 (synced Jul 11, 2026).
Common questions
- What is the difference between LLMs-from-scratch and PROMPTPurify?
- LLMs-from-scratch: Implement a ChatGPT-like LLM in PyTorch from scratch, step by step. PROMPTPurify: Prompt-injection guardrail for LLM applications. Compact model that outperforms larger open-source guards. No regex, no signatures. Demo: anton.securelayer7.net. See the comparison table for live GitHub stats and shared categories.
- When should I choose LLMs-from-scratch over PROMPTPurify?
- Choose LLMs-from-scratch over PROMPTPurify when LLMs-from-scratch is primarily Jupyter Notebook; PROMPTPurify is TypeScript; License: LLMs-from-scratch is Other, PROMPTPurify is MIT; Tags unique to LLMs-from-scratch: ai, artificial-intelligence, attention-mechanism, deep-learning; - You are an advanced practitioner aiming to fully understand the underpinnings of LLMs using PyTorch as your primary framework.
- When should I choose PROMPTPurify over LLMs-from-scratch?
- Choose PROMPTPurify over LLMs-from-scratch when PROMPTPurify is primarily TypeScript; LLMs-from-scratch is Jupyter Notebook; License: PROMPTPurify is MIT, LLMs-from-scratch is Other; Tags unique to PROMPTPurify: ai-firewall, ai-safety, ai-security, application-security; Also covers Computer Vision.
- 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.
- When should I avoid PROMPTPurify?
- 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.
- Is LLMs-from-scratch or PROMPTPurify more popular on GitHub?
- LLMs-from-scratch has more GitHub stars (98,899 vs 76). Stars measure visibility, not whether either tool fits your constraints.
- Are LLMs-from-scratch and PROMPTPurify open source?
- Yes - both are open-source projects on GitHub (LLMs-from-scratch: Other, PROMPTPurify: MIT).
- Where can I find alternatives to LLMs-from-scratch or PROMPTPurify?
- GraphCanon lists graph-backed alternatives at LLMs-from-scratch alternatives and PROMPTPurify alternatives (LLMs-from-scratch markdown twin, PROMPTPurify 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 PROMPTPurify?
- LLMs-from-scratch: Steady. PROMPTPurify: 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 LLMs-from-scratch and PROMPTPurify?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: LLMs-from-scratch trust report; PROMPTPurify trust report.