Comparison
mlrun vs LLMs-from-scratch
Verdict
Pick mlrun when mlrun is primarily Python; LLMs-from-scratch is Jupyter Notebook; pick LLMs-from-scratch when lLMs-from-scratch is primarily Jupyter Notebook; mlrun is Python.
Markdown twin · mlrun alternatives · LLMs-from-scratch alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | mlrun | LLMs-from-scratch |
|---|---|---|
| Maintenance | Very active (1d 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) | 8 low (8 low) As of today · osv@v1 | No lockfile As of today · none |
Tagline
- mlrun
- MLRun is an open source MLOps platform for quickly building and managing continuous ML applications across their lifecycle. MLRun integrates into your development and CI/CD environment and automates t
- LLMs-from-scratch
- Implement a ChatGPT-like LLM in PyTorch from scratch, step by step
Stars
- mlrun
- 1.7k
- LLMs-from-scratch
- 99k
Forks
- mlrun
- 308
- LLMs-from-scratch
- 15k
Open issues
- mlrun
- 104
- LLMs-from-scratch
- 4
Language
- mlrun
- Python
- LLMs-from-scratch
- Jupyter Notebook
Adopt for
- mlrun
- -
- 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
- mlrun
- -
- LLMs-from-scratch
- -
Runtime
- mlrun
- -
- LLMs-from-scratch
- -
License
- mlrun
- Apache-2.0
- LLMs-from-scratch
- Other
Last pushed
- mlrun
- Jul 10, 2026
- LLMs-from-scratch
- Jun 2, 2026
Categories
- mlrun
- AI Agents, LLM Frameworks, Model Training
- LLMs-from-scratch
- Model Training, LLM Frameworks
Trust and health
Maintenance
- mlrun
- Very active (96%)
- LLMs-from-scratch
- Steady (60%)
Days since push
- mlrun
- 1d
- LLMs-from-scratch
- 38d
Open issues (now)
- mlrun
- 104
- LLMs-from-scratch
- 4
Owner type
- mlrun
- Organization
- LLMs-from-scratch
- User
Security scan
- mlrun
- 8 low (8 low)
- LLMs-from-scratch
- No lockfile
Full report
- mlrun
- Trust report
- LLMs-from-scratch
- Trust report
Choose mlrun if…
- mlrun is primarily Python; LLMs-from-scratch is Jupyter Notebook.
- License: mlrun is Apache-2.0, LLMs-from-scratch is Other.
- Tags unique to mlrun: mlops-workflow, data-science, experiment-tracking, data-engineering.
- Also covers AI Agents.
When NOT to use mlrun
- AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism.
- 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.
Choose LLMs-from-scratch if…
- LLMs-from-scratch is primarily Jupyter Notebook; mlrun is Python.
- License: LLMs-from-scratch is Other, mlrun is Apache-2.0.
- Tags unique to LLMs-from-scratch: deep-learning, ai, artificial-intelligence, attention-mechanism.
- - 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 (mlrun/mlrun) · observed Jul 11, 2026
- GitHub forks (mlrun/mlrun) · observed Jul 11, 2026
- Last push (mlrun/mlrun) · observed Jul 10, 2026
- License file (Apache-2.0) · 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: mlrun 1.7k · LLMs-from-scratch 99k (synced Jul 11, 2026).
Common questions
- What is the difference between mlrun and LLMs-from-scratch?
- mlrun: MLRun is an open source MLOps platform for quickly building and managing continuous ML applications across their lifecycle. MLRun integrates into your development and CI/CD environment and automates t. 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 mlrun over LLMs-from-scratch?
- Choose mlrun over LLMs-from-scratch when mlrun is primarily Python; LLMs-from-scratch is Jupyter Notebook; License: mlrun is Apache-2.0, LLMs-from-scratch is Other; Tags unique to mlrun: mlops-workflow, data-science, experiment-tracking, data-engineering; Also covers AI Agents.
- When should I choose LLMs-from-scratch over mlrun?
- Choose LLMs-from-scratch over mlrun when LLMs-from-scratch is primarily Jupyter Notebook; mlrun is Python; License: LLMs-from-scratch is Other, mlrun is Apache-2.0; Tags unique to LLMs-from-scratch: deep-learning, ai, artificial-intelligence, attention-mechanism; - You are an advanced practitioner aiming to fully understand the underpinnings of LLMs using PyTorch as your primary framework.
- When should I avoid mlrun?
- AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. 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.
- 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 mlrun or LLMs-from-scratch more popular on GitHub?
- LLMs-from-scratch has more GitHub stars (98,899 vs 1,684). Stars measure visibility, not whether either tool fits your constraints.
- Are mlrun and LLMs-from-scratch open source?
- Yes - both are open-source projects on GitHub (mlrun: Apache-2.0, LLMs-from-scratch: Other).
- Where can I find alternatives to mlrun or LLMs-from-scratch?
- GraphCanon lists graph-backed alternatives at mlrun alternatives and LLMs-from-scratch alternatives (mlrun 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, mlrun or LLMs-from-scratch?
- mlrun: Very 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 mlrun and LLMs-from-scratch?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: mlrun trust report; LLMs-from-scratch trust report.