Comparison
FineTuningLLMs vs FastEdit
Verdict
Pick FineTuningLLMs if fineTuningLLMs is designed for users familiar with PyTorch and Hugging Face who seek practical guidance via Jupyter Notebooks; pick FastEdit if fastEdit is a Python library for quick edits to large language models using PyTorch.
Markdown twin · FineTuningLLMs alternatives · FastEdit alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | FineTuningLLMs | FastEdit |
|---|---|---|
| Maintenance | Slowing (146d since push) As of 3w · github_public_v1 | Dormant (1086d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 3w · 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 | 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'
- FastEdit
- Editing large language models within 10 seconds
Stars
- FineTuningLLMs
- 851
- FastEdit
- 1.4k
Forks
- FineTuningLLMs
- 114
- FastEdit
- 103
Open issues
- FineTuningLLMs
- 4
- FastEdit
- 21
Language
- FineTuningLLMs
- Jupyter Notebook
- FastEdit
- Python
Adopt for
- FineTuningLLMs
- FineTuningLLMs is designed for users familiar with PyTorch and Hugging Face who seek practical guidance via Jupyter Notebooks.
- FastEdit
- FastEdit is a Python library for quick edits to large language models using PyTorch.
Persona
- FineTuningLLMs
- -
- FastEdit
- -
Runtime
- FineTuningLLMs
- -
- FastEdit
- -
License
- FineTuningLLMs
- MIT
- FastEdit
- Apache-2.0
Last pushed
- FineTuningLLMs
- Feb 28, 2026
- FastEdit
- Aug 13, 2023
Categories
- FineTuningLLMs
- LLM Frameworks, Model Training
- FastEdit
- LLM Frameworks
Trust and health
Maintenance
- FineTuningLLMs
- Slowing (36%)
- FastEdit
- Dormant (18%)
Days since push
- FineTuningLLMs
- 146d
- FastEdit
- 1086d
Open issues (now)
- FineTuningLLMs
- 4
- FastEdit
- 21
OSV dependency advisories
- FineTuningLLMs
- No lockfile (source not queried)
- FastEdit
- Published findings
Full report
- FineTuningLLMs
- Trust report
- FastEdit
- Trust report
Choose FineTuningLLMs if…
- FineTuningLLMs is primarily Jupyter Notebook; FastEdit is Python.
- License: FineTuningLLMs is MIT, FastEdit 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 FastEdit if…
- FastEdit is primarily Python; FineTuningLLMs is Jupyter Notebook.
- License: FastEdit is Apache-2.0, FineTuningLLMs is MIT.
- Requirements: Min -1 GB RAM; Requires Python 3.8+ and PyTorch 1.13.1+. Must also install 🤗Transformers, Datasets, Accelerate, sentencepiece, and fire.; Hardware requirements for a specific model can vary; refer to the provided table for minimum RAM sizes for different models..
- Tags unique to FastEdit: bloom, chatbots, chatgpt, falcon.
- When rapid iterations on language model edits are necessary, such as testing and tuning with tight feedback loops.
When NOT to use FastEdit
- If your workflow requires integration with TensorFlow instead of PyTorch, since FastEdit is built on top of PyTorch.
- For hardware configurations that cannot meet the fast editing mode's requirements; for instance, if you have less than 24GB RAM available.
- If rapid edits within seconds are not a priority and longer processing times can be tolerated.
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 (hiyouga/FastEdit) · observed Aug 3, 2026
- GitHub forks (hiyouga/FastEdit) · observed Aug 3, 2026
- Last push (hiyouga/FastEdit) · observed Aug 13, 2023
- License file (Apache-2.0) · observed Aug 3, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: FineTuningLLMs 851 · FastEdit 1.4k (synced Jul 24, 2026).
Common questions
- What is the difference between FineTuningLLMs and FastEdit?
- FineTuningLLMs: Official repository for 'A Hands-On Guide to Fine-Tuning LLMs with PyTorch and Hugging Face'. FastEdit: Editing large language models within 10 seconds. See the comparison table for live GitHub stats and shared categories.
- When should I choose FineTuningLLMs over FastEdit?
- Choose FineTuningLLMs over FastEdit when FineTuningLLMs is primarily Jupyter Notebook; FastEdit is Python; License: FineTuningLLMs is MIT, FastEdit 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 FastEdit over FineTuningLLMs?
- Choose FastEdit over FineTuningLLMs when FastEdit is primarily Python; FineTuningLLMs is Jupyter Notebook; License: FastEdit is Apache-2.0, FineTuningLLMs is MIT; Requirements: Min -1 GB RAM; Requires Python 3.8+ and PyTorch 1.13.1+. Must also install 🤗Transformers, Datasets, Accelerate, sentencepiece, and fire.; Hardware requirements for a specific model can vary; refer to the provided table for minimum RAM sizes for different models.; Tags unique to FastEdit: bloom, chatbots, chatgpt, falcon; When rapid iterations on language model edits are necessary, such as testing and tuning with tight feedback loops.
- When should I avoid FineTuningLLMs?
- Not interested in PyTorch; prefer TensorFlow or another framework Seek theoretical background over practical applications
- When should I avoid FastEdit?
- If your workflow requires integration with TensorFlow instead of PyTorch, since FastEdit is built on top of PyTorch. For hardware configurations that cannot meet the fast editing mode's requirements; for instance, if you have less than 24GB RAM available. If rapid edits within seconds are not a priority and longer processing times can be tolerated.
- Is FineTuningLLMs or FastEdit more popular on GitHub?
- FastEdit has more GitHub stars (1,370 vs 851). Stars measure visibility, not whether either tool fits your constraints.
- Are FineTuningLLMs and FastEdit open source?
- Yes - both are open-source projects on GitHub (FineTuningLLMs: MIT, FastEdit: Apache-2.0).
- Where can I find alternatives to FineTuningLLMs or FastEdit?
- GraphCanon lists graph-backed alternatives at FineTuningLLMs alternatives and FastEdit alternatives (FineTuningLLMs markdown twin, FastEdit 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 FastEdit?
- FineTuningLLMs: Slowing. FastEdit: 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 FineTuningLLMs and FastEdit?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: FineTuningLLMs trust report; FastEdit trust report.