Comparison
LLMs-from-scratch vs ARES
Verdict
Pick LLMs-from-scratch when lLMs-from-scratch is primarily Jupyter Notebook; ARES is Python; pick ARES when aRES is primarily Python; LLMs-from-scratch is Jupyter Notebook.
Markdown twin · LLMs-from-scratch alternatives · ARES alternatives
GraphCanon updated today
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Trust & integrity
| Signal | LLMs-from-scratch | ARES |
|---|---|---|
| Maintenance | Steady (38d since push) As of today · github_public_v1 | Dormant (470d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Personal account As of today · github_public_v1 | Not a fork · Organization account As of today · github_public_v1 |
| Security (OSV) | No lockfile As of today · none | 154 low (154 low) As of today · osv@v1 |
Tagline
- LLMs-from-scratch
- Implement a ChatGPT-like LLM in PyTorch from scratch, step by step
- ARES
- Automated Evaluation of RAG Systems
Stars
- LLMs-from-scratch
- 99k
- ARES
- 724
Forks
- LLMs-from-scratch
- 15k
- ARES
- 66
Open issues
- LLMs-from-scratch
- 4
- ARES
- 21
Language
- LLMs-from-scratch
- Jupyter Notebook
- ARES
- Python
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.
- ARES
- -
Persona
- LLMs-from-scratch
- -
- ARES
- -
Runtime
- LLMs-from-scratch
- -
- ARES
- -
License
- LLMs-from-scratch
- Other
- ARES
- Apache-2.0
Last pushed
- LLMs-from-scratch
- Jun 2, 2026
- ARES
- Mar 28, 2025
Categories
- LLMs-from-scratch
- Model Training, LLM Frameworks
- ARES
- Model Training, LLM Frameworks, Vector Databases
Trust and health
Maintenance
- LLMs-from-scratch
- Steady (60%)
- ARES
- Dormant (18%)
Days since push
- LLMs-from-scratch
- 38d
- ARES
- 470d
Open issues (now)
- LLMs-from-scratch
- 4
- ARES
- 21
Owner type
- LLMs-from-scratch
- User
- ARES
- Organization
Security scan
- LLMs-from-scratch
- No lockfile
- ARES
- 154 low (154 low)
Full report
- LLMs-from-scratch
- Trust report
- ARES
- Trust report
Choose LLMs-from-scratch if…
- LLMs-from-scratch is primarily Jupyter Notebook; ARES is Python.
- License: LLMs-from-scratch is Other, ARES 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.
Choose ARES if…
- ARES is primarily Python; LLMs-from-scratch is Jupyter Notebook.
- License: ARES is Apache-2.0, LLMs-from-scratch is Other.
- Tags unique to ARES: python.
- Also covers Vector Databases.
When NOT to use ARES
- Last GitHub push was 470 days ago (dormant maintenance, Mar 28, 2025). Validate activity before betting a new project on ARES.
- 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.
- Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.
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 (stanford-futuredata/ARES) · observed Jul 11, 2026
- GitHub forks (stanford-futuredata/ARES) · observed Jul 11, 2026
- Last push (stanford-futuredata/ARES) · observed Mar 28, 2025
- License file (Apache-2.0) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: LLMs-from-scratch 99k · ARES 724 (synced Jul 11, 2026).
Common questions
- What is the difference between LLMs-from-scratch and ARES?
- LLMs-from-scratch: Implement a ChatGPT-like LLM in PyTorch from scratch, step by step. ARES: Automated Evaluation of RAG Systems. See the comparison table for live GitHub stats and shared categories.
- When should I choose LLMs-from-scratch over ARES?
- Choose LLMs-from-scratch over ARES when LLMs-from-scratch is primarily Jupyter Notebook; ARES is Python; License: LLMs-from-scratch is Other, ARES 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 choose ARES over LLMs-from-scratch?
- Choose ARES over LLMs-from-scratch when ARES is primarily Python; LLMs-from-scratch is Jupyter Notebook; License: ARES is Apache-2.0, LLMs-from-scratch is Other; Tags unique to ARES: python; Also covers Vector Databases.
- 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 ARES?
- Last GitHub push was 470 days ago (dormant maintenance, Mar 28, 2025). Validate activity before betting a new project on ARES. 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. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.
- Is LLMs-from-scratch or ARES more popular on GitHub?
- LLMs-from-scratch has more GitHub stars (98,899 vs 724). Stars measure visibility, not whether either tool fits your constraints.
- Are LLMs-from-scratch and ARES open source?
- Yes - both are open-source projects on GitHub (LLMs-from-scratch: Other, ARES: Apache-2.0).
- Where can I find alternatives to LLMs-from-scratch or ARES?
- GraphCanon lists graph-backed alternatives at LLMs-from-scratch alternatives and ARES alternatives (LLMs-from-scratch markdown twin, ARES 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 ARES?
- LLMs-from-scratch: Steady. ARES: 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 ARES?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: LLMs-from-scratch trust report; ARES trust report.