Comparison
ludwig vs LLMs-from-scratch
Verdict
Pick ludwig if ludwig is a low-code framework that simplifies the process of training deep learning models including custom LLMs and neural networks using Python; pick LLMs-from-scratch if 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.
Markdown twin · ludwig alternatives · LLMs-from-scratch alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | ludwig | LLMs-from-scratch |
|---|---|---|
| Maintenance | Active (7d since push) As of today · github_public_v1 | Steady (38d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Organization 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
- ludwig
- Low-code framework for building custom LLMs, neural networks, and other AI models
- LLMs-from-scratch
- Implement a ChatGPT-like LLM in PyTorch from scratch, step by step
Stars
- ludwig
- 12k
- LLMs-from-scratch
- 99k
Forks
- ludwig
- 1.2k
- LLMs-from-scratch
- 15k
Open issues
- ludwig
- 1
- LLMs-from-scratch
- 4
Language
- ludwig
- Python
- LLMs-from-scratch
- Jupyter Notebook
Adopt for
- ludwig
- Ludwig is a low-code framework that simplifies the process of training deep learning models including custom LLMs and neural networks using Python.
- 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.
Persona
- ludwig
- -
- LLMs-from-scratch
- -
Runtime
- ludwig
- -
- LLMs-from-scratch
- -
License
- ludwig
- Apache-2.0: Permissive open-source license allowing free use in both community and commercial projects.
- LLMs-from-scratch
- Other
Last pushed
- ludwig
- Jul 4, 2026
- LLMs-from-scratch
- Jun 2, 2026
Categories
- ludwig
- LLM Frameworks, Model Training, Computer Vision
- LLMs-from-scratch
- Model Training, LLM Frameworks
Trust and health
Maintenance
- ludwig
- Active (82%)
- LLMs-from-scratch
- Steady (60%)
Days since push
- ludwig
- 7d
- LLMs-from-scratch
- 38d
Open issues (now)
- ludwig
- 1
- LLMs-from-scratch
- 4
Owner type
- ludwig
- Organization
- LLMs-from-scratch
- User
Full report
- ludwig
- Trust report
- LLMs-from-scratch
- Trust report
Choose ludwig if…
- ludwig is primarily Python; LLMs-from-scratch is Jupyter Notebook.
- License: ludwig is Apache-2.0, LLMs-from-scratch is Other.
- Requirements: Min 4 GB RAM; Requires Python and is compatible with popular deep learning libraries like PyTorch..
- Tags unique to ludwig: data-science, deep, fine-tuning, learning.
- Also covers Computer Vision.
- When you need to build custom language models (LLMs) or other AI models with minimal configuration in Python.
When NOT to use ludwig
- If you require direct access and extensive customization of the model architecture, as Ludwig abstracts some of these details away under its low-code interface.
- When your team prefers a high-level of control over all aspects of the training process, including architectural decisions; Ludwig streamlines this process which may limit flexible adjustments.
Choose LLMs-from-scratch if…
- LLMs-from-scratch is primarily Jupyter Notebook; ludwig is Python.
- License: LLMs-from-scratch is Other, ludwig is Apache-2.0.
- Tags unique to LLMs-from-scratch: ai, artificial-intelligence, attention-mechanism, from-scratch.
- - 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (ludwig-ai/ludwig) · observed Jul 11, 2026
- GitHub forks (ludwig-ai/ludwig) · observed Jul 11, 2026
- Last push (ludwig-ai/ludwig) · observed Jul 4, 2026
- License file (Apache-2.0) · observed Jul 11, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- 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 on cards: ludwig 12k · LLMs-from-scratch 99k (synced Jul 11, 2026).
Common questions
- What is the difference between ludwig and LLMs-from-scratch?
- ludwig: Low-code framework for building custom LLMs, neural networks, and other AI 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 ludwig over LLMs-from-scratch?
- Choose ludwig over LLMs-from-scratch when ludwig is primarily Python; LLMs-from-scratch is Jupyter Notebook; License: ludwig is Apache-2.0, LLMs-from-scratch is Other; Requirements: Min 4 GB RAM; Requires Python and is compatible with popular deep learning libraries like PyTorch.; Tags unique to ludwig: data-science, deep, fine-tuning, learning; Also covers Computer Vision; When you need to build custom language models (LLMs) or other AI models with minimal configuration in Python.
- When should I choose LLMs-from-scratch over ludwig?
- Choose LLMs-from-scratch over ludwig when LLMs-from-scratch is primarily Jupyter Notebook; ludwig is Python; License: LLMs-from-scratch is Other, ludwig is Apache-2.0; Tags unique to LLMs-from-scratch: ai, artificial-intelligence, attention-mechanism, from-scratch; - You are an advanced practitioner aiming to fully understand the underpinnings of LLMs using PyTorch as your primary framework.
- When should I avoid ludwig?
- If you require direct access and extensive customization of the model architecture, as Ludwig abstracts some of these details away under its low-code interface. When your team prefers a high-level of control over all aspects of the training process, including architectural decisions; Ludwig streamlines this process which may limit flexible adjustments.
- 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.
- Is ludwig or LLMs-from-scratch more popular on GitHub?
- LLMs-from-scratch has more GitHub stars (98,899 vs 11,734). Stars measure visibility, not whether either tool fits your constraints.
- Are ludwig and LLMs-from-scratch open source?
- Yes - both are open-source projects on GitHub (ludwig: Apache-2.0, LLMs-from-scratch: Other).
- Where can I find alternatives to ludwig or LLMs-from-scratch?
- GraphCanon lists graph-backed alternatives at ludwig alternatives and LLMs-from-scratch alternatives (ludwig 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, ludwig or LLMs-from-scratch?
- ludwig: Active. 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 ludwig and LLMs-from-scratch?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: ludwig trust report; LLMs-from-scratch trust report.