Comparison
FineTuningLLMs vs TinyZero
Verdict
Pick FineTuningLLMs if fineTuningLLMs is designed for users familiar with PyTorch and Hugging Face who seek practical guidance via Jupyter Notebooks; pick TinyZero if tinyZero is a scaled-down version of the R1-Zero architecture from DeepSeek, focusing on minimal setup with essential components.
Markdown twin · FineTuningLLMs alternatives · TinyZero alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | FineTuningLLMs | TinyZero |
|---|---|---|
| Maintenance | Slowing (176d since push) As of today · github_public_v1 | Slowing (160d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Personal account As of today · github_public_v1 | Not a fork · Personal account As of 2w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No published findings from this source as of 2026-07-11 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'
- TinyZero
- Minimal reproduction of DeepSeek R1-Zero
Stars
- FineTuningLLMs
- 855
- TinyZero
- 13k
Forks
- FineTuningLLMs
- 116
- TinyZero
- 1.6k
Open issues
- FineTuningLLMs
- 4
- TinyZero
- 82
Language
- FineTuningLLMs
- Jupyter Notebook
- TinyZero
- Python
Adopt for
- FineTuningLLMs
- FineTuningLLMs is designed for users familiar with PyTorch and Hugging Face who seek practical guidance via Jupyter Notebooks.
- TinyZero
- TinyZero is a scaled-down version of the R1-Zero architecture from DeepSeek, focusing on minimal setup with essential components.
Persona
- FineTuningLLMs
- -
- TinyZero
- -
Runtime
- FineTuningLLMs
- -
- TinyZero
- -
License
- FineTuningLLMs
- MIT
- TinyZero
- TinyZero is licensed under Apache-2.0, allowing for broad usage with attribution requirements.
Last pushed
- FineTuningLLMs
- Feb 28, 2026
- TinyZero
- Feb 27, 2026
Categories
- FineTuningLLMs
- LLM Frameworks, Model Training
- TinyZero
- LLM Frameworks
Trust and health
Days since push
- FineTuningLLMs
- 176d
- TinyZero
- 160d
Open issues (now)
- FineTuningLLMs
- 4
- TinyZero
- 82
Stars delta
- FineTuningLLMs
- +4 (30d)
- TinyZero
- Unknown
Open issues delta
- FineTuningLLMs
- 0 (30d)
- TinyZero
- Unknown
OSV dependency advisories
- FineTuningLLMs
- No lockfile (source not queried)
- TinyZero
- No published findings from this source as of 2026-07-11
Full report
- FineTuningLLMs
- Trust report
- TinyZero
- Trust report
Choose FineTuningLLMs if…
- FineTuningLLMs is primarily Jupyter Notebook; TinyZero is Python.
- License: FineTuningLLMs is MIT, TinyZero is Apache-2.0.
- Tags unique to FineTuningLLMs: bitsandbytes, fine-tuning, finetuning, hugging-face.
- Also covers Model Training.
- 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 TinyZero if…
- TinyZero is primarily Python; FineTuningLLMs is Jupyter Notebook.
- License: TinyZero is Apache-2.0, FineTuningLLMs is MIT.
- Pricing: The framework itself is free and can be used without charge;.
- Requirements: Min 4 GB RAM; Specific Python environment setup (Python 3.9) and dependency installation steps are outlined in the README..
- Tags unique to TinyZero: deepseek, r1-zero, ray, vllm.
- When you need a streamlined implementation of the R1-Zero architecture without unnecessary complexity.
When NOT to use TinyZero
- If your project demands extensive customization options not available in this minimal version.
- When working with environments where specific versions of PyTorch older than 2.4.0 are required, as TinyZero mandates the use of PyTorch 2.4.0 or allows vLLM to manage its installation.
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 Aug 24, 2026
- GitHub forks (dvgodoy/FineTuningLLMs) · observed Aug 24, 2026
- Last push (dvgodoy/FineTuningLLMs) · observed Feb 28, 2026
- License file (MIT) · observed Aug 24, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (Jiayi-Pan/TinyZero) · observed Aug 6, 2026
- GitHub forks (Jiayi-Pan/TinyZero) · observed Aug 6, 2026
- Last push (Jiayi-Pan/TinyZero) · observed Feb 27, 2026
- License file (Apache-2.0) · observed Aug 6, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: FineTuningLLMs 855 · TinyZero 13k (synced Aug 24, 2026).
Common questions
- What is the difference between FineTuningLLMs and TinyZero?
- FineTuningLLMs: Official repository for 'A Hands-On Guide to Fine-Tuning LLMs with PyTorch and Hugging Face'. TinyZero: Minimal reproduction of DeepSeek R1-Zero. See the comparison table for live GitHub stats and shared categories.
- When should I choose FineTuningLLMs over TinyZero?
- Choose FineTuningLLMs over TinyZero when FineTuningLLMs is primarily Jupyter Notebook; TinyZero is Python; License: FineTuningLLMs is MIT, TinyZero is Apache-2.0; Tags unique to FineTuningLLMs: bitsandbytes, fine-tuning, finetuning, hugging-face; Also covers Model Training; You need hands-on, step-by-step instructions using PyTorch and the Hugging Face ecosystem.
- When should I choose TinyZero over FineTuningLLMs?
- Choose TinyZero over FineTuningLLMs when TinyZero is primarily Python; FineTuningLLMs is Jupyter Notebook; License: TinyZero is Apache-2.0, FineTuningLLMs is MIT; Pricing: The framework itself is free and can be used without charge;; Requirements: Min 4 GB RAM; Specific Python environment setup (Python 3.9) and dependency installation steps are outlined in the README.; Tags unique to TinyZero: deepseek, r1-zero, ray, vllm; When you need a streamlined implementation of the R1-Zero architecture without unnecessary complexity.
- When should I avoid FineTuningLLMs?
- Not interested in PyTorch; prefer TensorFlow or another framework Seek theoretical background over practical applications
- When should I avoid TinyZero?
- If your project demands extensive customization options not available in this minimal version. When working with environments where specific versions of PyTorch older than 2.4.0 are required, as TinyZero mandates the use of PyTorch 2.4.0 or allows vLLM to manage its installation.
- Is FineTuningLLMs or TinyZero more popular on GitHub?
- TinyZero has more GitHub stars (13,214 vs 855). Stars measure visibility, not whether either tool fits your constraints.
- Are FineTuningLLMs and TinyZero open source?
- Yes - both are open-source projects on GitHub (FineTuningLLMs: MIT, TinyZero: Apache-2.0).
- Where can I find alternatives to FineTuningLLMs or TinyZero?
- GraphCanon lists graph-backed alternatives at FineTuningLLMs alternatives and TinyZero alternatives (FineTuningLLMs markdown twin, TinyZero 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 TinyZero?
- FineTuningLLMs: Slowing. TinyZero: Slowing. 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 TinyZero?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: FineTuningLLMs trust report; TinyZero trust report.