Comparison
FineTuningLLMs vs reasoning-from-scratch
Verdict
Pick FineTuningLLMs if fineTuningLLMs is designed for users familiar with PyTorch and Hugging Face who seek practical guidance via Jupyter Notebooks; pick reasoning-from-scratch if a step-by-step guide to building a reasoning large language model (LLM) using PyTorch, suitable for running on consumer hardware with automatic GPU utilization.
Markdown twin · FineTuningLLMs alternatives · reasoning-from-scratch alternatives
GraphCanon updated 4d
vs
Trust & integrity
| Signal | FineTuningLLMs | reasoning-from-scratch |
|---|---|---|
| Maintenance | Slowing (146d since push) As of 4w · github_public_v1 | Active (12d since push) As of 4d · github_public_v1 |
| Provenance | Not a fork · Personal account As of 4w · github_public_v1 | Not a fork · Personal account As of 4d · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | Published findings As of 1mo · 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
- FineTuningLLMs
- Official repository for 'A Hands-On Guide to Fine-Tuning LLMs with PyTorch and Hugging Face'
- reasoning-from-scratch
- Implement a reasoning LLM in PyTorch from scratch, step by step
Stars
- FineTuningLLMs
- 851
- reasoning-from-scratch
- 5.0k
Forks
- FineTuningLLMs
- 114
- reasoning-from-scratch
- 759
Open issues
- FineTuningLLMs
- 4
- reasoning-from-scratch
- 2
Language
- FineTuningLLMs
- Jupyter Notebook
- reasoning-from-scratch
- Jupyter Notebook
Adopt for
- FineTuningLLMs
- FineTuningLLMs is designed for users familiar with PyTorch and Hugging Face who seek practical guidance via Jupyter Notebooks.
- reasoning-from-scratch
- A step-by-step guide to building a reasoning large language model (LLM) using PyTorch, suitable for running on consumer hardware with automatic GPU utilization.
Persona
- FineTuningLLMs
- -
- reasoning-from-scratch
- -
Runtime
- FineTuningLLMs
- -
- reasoning-from-scratch
- -
License
- FineTuningLLMs
- MIT
- reasoning-from-scratch
- Apache-2.0 License
Last pushed
- FineTuningLLMs
- Feb 28, 2026
- reasoning-from-scratch
- Aug 4, 2026
Categories
- FineTuningLLMs
- LLM Frameworks, Model Training
- reasoning-from-scratch
- LLM Frameworks, Model Training
Trust and health
Maintenance
- FineTuningLLMs
- Slowing (36%)
- reasoning-from-scratch
- Active (82%)
Days since push
- FineTuningLLMs
- 146d
- reasoning-from-scratch
- 12d
Open issues (now)
- FineTuningLLMs
- 4
- reasoning-from-scratch
- 2
Stars delta
- FineTuningLLMs
- Unknown
- reasoning-from-scratch
- +252 (30d)
Open issues delta
- FineTuningLLMs
- Unknown
- reasoning-from-scratch
- 0 (30d)
OSV dependency advisories
- FineTuningLLMs
- No lockfile (source not queried)
- reasoning-from-scratch
- Published findings
Full report
- FineTuningLLMs
- Trust report
- reasoning-from-scratch
- Trust report
Choose FineTuningLLMs if…
- License: FineTuningLLMs is MIT, reasoning-from-scratch is Apache-2.0.
- Tags unique to FineTuningLLMs: bitsandbytes, fine-tuning, finetuning, hugging-face.
- You need hands-on, step-by-step instructions using PyTorch and the Hugging Face ecosystem
When NOT to use FineTuningLLMs
- Not interested in PyTorch; prefer TensorFlow or another framework
- Seek theoretical background over practical applications
Choose reasoning-from-scratch if…
- License: reasoning-from-scratch is Apache-2.0, FineTuningLLMs is MIT.
- Requirements: Automatic GPU utilization where available, though not strictly necessary for the early chapters..
- Tags unique to reasoning-from-scratch: ai, artificial-intelligence, chain-of-thought, deep-learning.
- When you have intermediate knowledge of PyTorch and want detailed insights into the implementation process of reasoning LLMS.
When NOT to use reasoning-from-scratch
- Avoid this tool if you are looking for rapid prototyping or quick model deployment; it focuses more on understanding and building the LLM from scratch rather than providing prebuilt components.
- If specialized server hardware is available and preferred for the entire project, as chapters 5 and 6 recommend GPU use but earlier sections can be completed with just a CPU.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (dvgodoy/FineTuningLLMs) · observed Jul 24, 2026
- GitHub forks (dvgodoy/FineTuningLLMs) · observed Jul 24, 2026
- Last push (dvgodoy/FineTuningLLMs) · observed Feb 28, 2026
- License file (MIT) · observed Jul 24, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (rasbt/reasoning-from-scratch) · observed Aug 17, 2026
- GitHub forks (rasbt/reasoning-from-scratch) · observed Aug 17, 2026
- Last push (rasbt/reasoning-from-scratch) · observed Aug 4, 2026
- License file (Apache-2.0) · observed Aug 17, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: FineTuningLLMs 851 · reasoning-from-scratch 5.0k (synced Jul 24, 2026).
Common questions
- What is the difference between FineTuningLLMs and reasoning-from-scratch?
- FineTuningLLMs: Official repository for 'A Hands-On Guide to Fine-Tuning LLMs with PyTorch and Hugging Face'. reasoning-from-scratch: Implement a reasoning LLM in PyTorch from scratch, step by step. See the comparison table for live GitHub stats and shared categories.
- When should I choose FineTuningLLMs over reasoning-from-scratch?
- Choose FineTuningLLMs over reasoning-from-scratch when License: FineTuningLLMs is MIT, reasoning-from-scratch is Apache-2.0; Tags unique to FineTuningLLMs: bitsandbytes, fine-tuning, finetuning, hugging-face; You need hands-on, step-by-step instructions using PyTorch and the Hugging Face ecosystem.
- When should I choose reasoning-from-scratch over FineTuningLLMs?
- Choose reasoning-from-scratch over FineTuningLLMs when License: reasoning-from-scratch is Apache-2.0, FineTuningLLMs is MIT; Requirements: Automatic GPU utilization where available, though not strictly necessary for the early chapters.; Tags unique to reasoning-from-scratch: ai, artificial-intelligence, chain-of-thought, deep-learning; When you have intermediate knowledge of PyTorch and want detailed insights into the implementation process of reasoning LLMS.
- When should I avoid FineTuningLLMs?
- Not interested in PyTorch; prefer TensorFlow or another framework Seek theoretical background over practical applications
- When should I avoid reasoning-from-scratch?
- Avoid this tool if you are looking for rapid prototyping or quick model deployment; it focuses more on understanding and building the LLM from scratch rather than providing prebuilt components. If specialized server hardware is available and preferred for the entire project, as chapters 5 and 6 recommend GPU use but earlier sections can be completed with just a CPU.
- Is FineTuningLLMs or reasoning-from-scratch more popular on GitHub?
- reasoning-from-scratch has more GitHub stars (4,998 vs 851). Stars measure visibility, not whether either tool fits your constraints.
- Are FineTuningLLMs and reasoning-from-scratch open source?
- Yes - both are open-source projects on GitHub (FineTuningLLMs: MIT, reasoning-from-scratch: Apache-2.0).
- Where can I find alternatives to FineTuningLLMs or reasoning-from-scratch?
- GraphCanon lists graph-backed alternatives at FineTuningLLMs alternatives and reasoning-from-scratch alternatives (FineTuningLLMs markdown twin, reasoning-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, FineTuningLLMs or reasoning-from-scratch?
- FineTuningLLMs: Slowing. reasoning-from-scratch: Active. 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 FineTuningLLMs and reasoning-from-scratch?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: FineTuningLLMs trust report; reasoning-from-scratch trust report.