Comparison
LlamaFactory vs Failed-ML
Verdict
Pick LlamaFactory when license: LlamaFactory is Apache-2.0, Failed-ML is MIT; pick Failed-ML when license: Failed-ML is MIT, LlamaFactory is Apache-2.0.
Markdown twin · LlamaFactory alternatives · Failed-ML alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | LlamaFactory | Failed-ML |
|---|---|---|
| Maintenance | Very active (0d since push) As of 1d · github_public_v1 | Dormant (757d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Personal account As of 1d · github_public_v1 | Not a fork · Personal account As of today · github_public_v1 |
| Security (OSV) | No lockfile As of 1d · none | No lockfile As of today · none |
Tagline
- LlamaFactory
- Unified Efficient Fine-Tuning of 100+ LLMs & VLMs
- Failed-ML
- Compilation of high-profile real-world examples of failed machine learning projects
Stars
- LlamaFactory
- 73k
- Failed-ML
- 753
Forks
- LlamaFactory
- 8.9k
- Failed-ML
- 51
Open issues
- LlamaFactory
- 1.1k
- Failed-ML
- 0
Language
- LlamaFactory
- Python
- Failed-ML
- -
Adopt for
- LlamaFactory
- LlamaFactory is a sophisticated tool for fine-tuning numerous large language models and visual language models efficiently using various methods such as LoRA, QLoRA, RLHF, and quantization.
- Failed-ML
- -
Persona
- LlamaFactory
- -
- Failed-ML
- -
Runtime
- LlamaFactory
- -
- Failed-ML
- -
License
- LlamaFactory
- Apache-2.0
- Failed-ML
- MIT
Last pushed
- LlamaFactory
- Jul 10, 2026
- Failed-ML
- Jun 14, 2024
Categories
- LlamaFactory
- LLM Frameworks, Model Training
- Failed-ML
- Computer Vision, LLM Frameworks, Model Training
Trust and health
Maintenance
- LlamaFactory
- Very active (96%)
- Failed-ML
- Dormant (18%)
Days since push
- LlamaFactory
- 0d
- Failed-ML
- 757d
Open issues (now)
- LlamaFactory
- 1.1k
- Failed-ML
- 0
Full report
- LlamaFactory
- Trust report
- Failed-ML
- Trust report
Choose LlamaFactory if…
- License: LlamaFactory is Apache-2.0, Failed-ML is MIT.
- Tags unique to LlamaFactory: agent, deepseek, fine-tuning, gemma.
- When you need to fine-tune over 100 different LLMs or VLMs with efficient methods like LoRA or QLoRA.
When NOT to use LlamaFactory
- When you are looking to fine-tune less popular or niche models that are not supported within the 100+ models covered by LlamaFactory.
- If your project specifically requires custom fine-tuning methods not available in this repository, such as certain versions of PEFT (Parameter Efficient Fine-Tuning) techniques excluding LoRA and QLoa
Choose Failed-ML if…
- License: Failed-ML is MIT, LlamaFactory is Apache-2.0.
- Tags unique to Failed-ML: artificial-intelligence, classification, computer-vision, data-engineering.
- Also covers Computer Vision.
When NOT to use Failed-ML
- Last GitHub push was 758 days ago (dormant maintenance, Jun 14, 2024). Validate activity before betting a new project on Failed-ML.
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
- Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (hiyouga/LlamaFactory) · observed Jul 11, 2026
- GitHub forks (hiyouga/LlamaFactory) · observed Jul 11, 2026
- Last push (hiyouga/LlamaFactory) · observed Jul 10, 2026
- License file (Apache-2.0) · observed Jul 11, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (kennethleungty/Failed-ML) · observed Jul 11, 2026
- GitHub forks (kennethleungty/Failed-ML) · observed Jul 11, 2026
- Last push (kennethleungty/Failed-ML) · observed Jun 14, 2024
- License file (MIT) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: LlamaFactory 73k · Failed-ML 753 (synced Jul 11, 2026).
Common questions
- What is the difference between LlamaFactory and Failed-ML?
- LlamaFactory: Unified Efficient Fine-Tuning of 100+ LLMs & VLMs. Failed-ML: Compilation of high-profile real-world examples of failed machine learning projects. See the comparison table for live GitHub stats and shared categories.
- When should I choose LlamaFactory over Failed-ML?
- Choose LlamaFactory over Failed-ML when License: LlamaFactory is Apache-2.0, Failed-ML is MIT; Tags unique to LlamaFactory: agent, deepseek, fine-tuning, gemma; When you need to fine-tune over 100 different LLMs or VLMs with efficient methods like LoRA or QLoRA.
- When should I choose Failed-ML over LlamaFactory?
- Choose Failed-ML over LlamaFactory when License: Failed-ML is MIT, LlamaFactory is Apache-2.0; Tags unique to Failed-ML: artificial-intelligence, classification, computer-vision, data-engineering; Also covers Computer Vision.
- When should I avoid LlamaFactory?
- When you are looking to fine-tune less popular or niche models that are not supported within the 100+ models covered by LlamaFactory. If your project specifically requires custom fine-tuning methods not available in this repository, such as certain versions of PEFT (Parameter Efficient Fine-Tuning) techniques excluding LoRA and QLoa
- When should I avoid Failed-ML?
- Last GitHub push was 758 days ago (dormant maintenance, Jun 14, 2024). Validate activity before betting a new project on Failed-ML. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.
- Is LlamaFactory or Failed-ML more popular on GitHub?
- LlamaFactory has more GitHub stars (73,157 vs 753). Stars measure visibility, not whether either tool fits your constraints.
- Are LlamaFactory and Failed-ML open source?
- Yes - both are open-source projects on GitHub (LlamaFactory: Apache-2.0, Failed-ML: MIT).
- Where can I find alternatives to LlamaFactory or Failed-ML?
- GraphCanon lists graph-backed alternatives at LlamaFactory alternatives and Failed-ML alternatives (LlamaFactory markdown twin, Failed-ML 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, LlamaFactory or Failed-ML?
- LlamaFactory: Very active. Failed-ML: 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 LlamaFactory and Failed-ML?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: LlamaFactory trust report; Failed-ML trust report.