Comparison
awesome-llms-fine-tuning vs LLMs-Finetuning-Safety
Verdict
Pick awesome-llms-fine-tuning if a curated list for LLM fine-tuning resources including tutorials, papers, and tools; pick LLMs-Finetuning-Safety if lLMs-Finetuning-Safety demonstrates the safety risks associated with fine-tuning GPT-3.5 Turbo using few adversarially designed examples.
Markdown twin · awesome-llms-fine-tuning alternatives · LLMs-Finetuning-Safety alternatives
GraphCanon updated 1d
vs
Trust & integrity
| Signal | awesome-llms-fine-tuning | LLMs-Finetuning-Safety |
|---|---|---|
| Maintenance | Dormant (629d since push) As of 1d · github_public_v1 | Dormant (893d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 1d · github_public_v1 | Not a fork · Personal account As of 3w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) 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
- awesome-llms-fine-tuning
- A comprehensive collection of resources for fine-tuning Large Language Models.
- LLMs-Finetuning-Safety
- Demonstrates safety risks in fine-tuning GPT-3.5 Turbo with adversarial examples
Stars
- awesome-llms-fine-tuning
- 525
- LLMs-Finetuning-Safety
- 358
Forks
- awesome-llms-fine-tuning
- 79
- LLMs-Finetuning-Safety
- 38
Open issues
- awesome-llms-fine-tuning
- 10
- LLMs-Finetuning-Safety
- 3
Language
- awesome-llms-fine-tuning
- -
- LLMs-Finetuning-Safety
- Python
Adopt for
- awesome-llms-fine-tuning
- A curated list for LLM fine-tuning resources including tutorials, papers, and tools.
- LLMs-Finetuning-Safety
- LLMs-Finetuning-Safety demonstrates the safety risks associated with fine-tuning GPT-3.5 Turbo using few adversarially designed examples.
Persona
- awesome-llms-fine-tuning
- -
- LLMs-Finetuning-Safety
- -
Runtime
- awesome-llms-fine-tuning
- -
- LLMs-Finetuning-Safety
- -
License
- awesome-llms-fine-tuning
- (unknown) - (unknown)
- LLMs-Finetuning-Safety
- MIT
Last pushed
- awesome-llms-fine-tuning
- Dec 2, 2024
- LLMs-Finetuning-Safety
- Feb 23, 2024
Categories
- awesome-llms-fine-tuning
- LLM Frameworks, Model Training
- LLMs-Finetuning-Safety
- Evaluation & Observability, Model Training
Trust and health
Days since push
- awesome-llms-fine-tuning
- 629d
- LLMs-Finetuning-Safety
- 893d
Open issues (now)
- awesome-llms-fine-tuning
- 10
- LLMs-Finetuning-Safety
- 3
Stars delta
- awesome-llms-fine-tuning
- 0 (30d)
- LLMs-Finetuning-Safety
- Unknown
Open issues delta
- awesome-llms-fine-tuning
- +1 (30d)
- LLMs-Finetuning-Safety
- Unknown
Owner type
- awesome-llms-fine-tuning
- Organization
- LLMs-Finetuning-Safety
- User
Full report
- awesome-llms-fine-tuning
- Trust report
- LLMs-Finetuning-Safety
- Trust report
Choose awesome-llms-fine-tuning if…
- Tags unique to awesome-llms-fine-tuning: ai, awesome-list, deep-learning, fine-tuning.
- Also covers LLM Frameworks.
- Need extensive guidance on LLM-specific fine-tuning strategies
When NOT to use awesome-llms-fine-tuning
- Looking for real-time interactive support or direct code implementation help
- Favor more specialized tools for immediate performance optimization over broad learning
Choose LLMs-Finetuning-Safety if…
- Pricing: Open-source under the MIT license; free to use and modify. OpenAI API usage cost applies, but this repository demonstrates effects at less than $0.20..
- Tags unique to LLMs-Finetuning-Safety: adversarial training, alignment, llm, llm-finetuning.
- Also covers Evaluation & Observability.
- When evaluating the risk of compromised safety in language models after fine-tuning them on small, carefully crafted datasets.
When NOT to use LLMs-Finetuning-Safety
- When generalizing safety risks to other large language models that have different underlying architectures or safeguard mechanisms than GPT-3.5 Turbo.
- If intending to use this tool as a method of fine-tuning any model for enhancing its performance on specific tasks, given it is designed for illustrating risk rather than improving capabilities.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (Curated-Awesome-Lists/awesome-llms-fine-tuning) · observed Aug 24, 2026
- GitHub forks (Curated-Awesome-Lists/awesome-llms-fine-tuning) · observed Aug 24, 2026
- Last push (Curated-Awesome-Lists/awesome-llms-fine-tuning) · observed Dec 2, 2024
- License file (unknown) · observed Aug 24, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (LLM-Tuning-Safety/LLMs-Finetuning-Safety) · observed Aug 5, 2026
- GitHub forks (LLM-Tuning-Safety/LLMs-Finetuning-Safety) · observed Aug 5, 2026
- Last push (LLM-Tuning-Safety/LLMs-Finetuning-Safety) · observed Feb 23, 2024
- License file (MIT) · observed Aug 5, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: awesome-llms-fine-tuning 525 · LLMs-Finetuning-Safety 358 (synced Aug 24, 2026).
Common questions
- What is the difference between awesome-llms-fine-tuning and LLMs-Finetuning-Safety?
- awesome-llms-fine-tuning: A comprehensive collection of resources for fine-tuning Large Language Models.. LLMs-Finetuning-Safety: Demonstrates safety risks in fine-tuning GPT-3.5 Turbo with adversarial examples. See the comparison table for live GitHub stats and shared categories.
- When should I choose awesome-llms-fine-tuning over LLMs-Finetuning-Safety?
- Choose awesome-llms-fine-tuning over LLMs-Finetuning-Safety when Tags unique to awesome-llms-fine-tuning: ai, awesome-list, deep-learning, fine-tuning; Also covers LLM Frameworks; Need extensive guidance on LLM-specific fine-tuning strategies.
- When should I choose LLMs-Finetuning-Safety over awesome-llms-fine-tuning?
- Choose LLMs-Finetuning-Safety over awesome-llms-fine-tuning when Pricing: Open-source under the MIT license; free to use and modify. OpenAI API usage cost applies, but this repository demonstrates effects at less than $0.20.; Tags unique to LLMs-Finetuning-Safety: adversarial training, alignment, llm, llm-finetuning; Also covers Evaluation & Observability; When evaluating the risk of compromised safety in language models after fine-tuning them on small, carefully crafted datasets.
- When should I avoid awesome-llms-fine-tuning?
- Looking for real-time interactive support or direct code implementation help Favor more specialized tools for immediate performance optimization over broad learning
- When should I avoid LLMs-Finetuning-Safety?
- When generalizing safety risks to other large language models that have different underlying architectures or safeguard mechanisms than GPT-3.5 Turbo. If intending to use this tool as a method of fine-tuning any model for enhancing its performance on specific tasks, given it is designed for illustrating risk rather than improving capabilities.
- Is awesome-llms-fine-tuning or LLMs-Finetuning-Safety more popular on GitHub?
- awesome-llms-fine-tuning has more GitHub stars (525 vs 358). Stars measure visibility, not whether either tool fits your constraints.
- Are awesome-llms-fine-tuning and LLMs-Finetuning-Safety open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to awesome-llms-fine-tuning or LLMs-Finetuning-Safety?
- GraphCanon lists graph-backed alternatives at awesome-llms-fine-tuning alternatives and LLMs-Finetuning-Safety alternatives (awesome-llms-fine-tuning markdown twin, LLMs-Finetuning-Safety 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, awesome-llms-fine-tuning or LLMs-Finetuning-Safety?
- awesome-llms-fine-tuning: Dormant. LLMs-Finetuning-Safety: 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 awesome-llms-fine-tuning and LLMs-Finetuning-Safety?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: awesome-llms-fine-tuning trust report; LLMs-Finetuning-Safety trust report.