Comparison
lightly-train vs GLiNER
Verdict
Pick lightly-train if lightly-train is a Python-based framework focused on training vision models including YOLO, ViTs, RT-DETR, and DINOv3, offering comprehensive features like pretraining, fine-tuning, and distillation; pick GLiNER if gLiNER is ideal for extracting named entities from text with minimal computational resources.
Markdown twin · lightly-train alternatives · GLiNER alternatives
GraphCanon updated Sep 20, 2026
7views this month
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Trust & integrity
| Signal | lightly-train | GLiNER |
|---|---|---|
| Maintenance | Very active (5d since push) As of Sep 20, 2026 · github_public_v1 | Active (7d since push) As of Aug 18, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 20, 2026 · github_public_v1 | Not a fork · Personal account As of Aug 18, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | Published findings As of Jul 11, 2026 · 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
- lightly-train
- All-in-one training for vision models: pretraining, fine-tuning, distillation.
- GLiNER
- Generalist and Lightweight Model for Named Entity Recognition
Stars
- lightly-train
- 1.7k
- GLiNER
- 3.5k
Forks
- lightly-train
- 116
- GLiNER
- 299
Open issues
- lightly-train
- 82
- GLiNER
- 96
Language
- lightly-train
- Python
- GLiNER
- Python
Adopt for
- lightly-train
- Lightly-train is a Python-based framework focused on training vision models including YOLO, ViTs, RT-DETR, and DINOv3, offering comprehensive features like pretraining, fine-tuning, and distillation.
- GLiNER
- GLiNER is ideal for extracting named entities from text with minimal computational resources.
Persona
- lightly-train
- -
- GLiNER
- -
Runtime
- lightly-train
- -
- GLiNER
- -
License
- lightly-train
- AGPL-3.0
- GLiNER
- Apache-2.0
Last pushed
- lightly-train
- Sep 14, 2026
- GLiNER
- Aug 10, 2026
Categories
- lightly-train
- Computer Vision, Model Training
- GLiNER
- Data & Retrieval, Model Training
Trust and health
Maintenance
- lightly-train
- Very active (96%)
- GLiNER
- Active (82%)
Days since push
- lightly-train
- 5d
- GLiNER
- 7d
Open issues (now)
- lightly-train
- 82
- GLiNER
- 96
Stars delta
- lightly-train
- +58 (30d)
- GLiNER
- +143 (30d)
Open issues delta
- lightly-train
- +19 (30d)
- GLiNER
- -1 (30d)
Owner type
- lightly-train
- Organization
- GLiNER
- User
OSV dependency advisories
- lightly-train
- No lockfile (source not queried)
- GLiNER
- Published findings
Full report
- lightly-train
- Trust report
- GLiNER
- Trust report
Choose lightly-train if…
- License: lightly-train is AGPL-3.0, GLiNER is Apache-2.0.
- Requirements: Min 8 GB RAM.
- Tags unique to lightly-train: computer-vision, contrastive-learning, deep-learning, depth-estimation.
- Also covers Computer Vision.
- Lightly-train is a Python-based framework focused on training vision models including YOLO, ViTs, RT-DETR, and DINOv3, offering comprehensive features like pretraining, fine-tuning, and distillation.
When NOT to use lightly-train
- Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.
Choose GLiNER if…
- License: GLiNER is Apache-2.0, lightly-train is AGPL-3.0.
- Tags unique to GLiNER: information-extraction, large-language-models, named-entity-recognition, natural language processing.
- Also covers Data & Retrieval.
- When you need a lightweight solution for named entity recognition across various languages
When NOT to use GLiNER
- If high precision in niche specializations like medical terms or rare proper nouns is required
- In scenarios demanding heavy customization beyond basic named entity recognition capabilities
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (lightly-ai/lightly-train) · observed Sep 20, 2026
- GitHub forks (lightly-ai/lightly-train) · observed Sep 20, 2026
- Last push (lightly-ai/lightly-train) · observed Sep 14, 2026
- License file (AGPL-3.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (urchade/GLiNER) · observed Sep 20, 2026
- GitHub forks (urchade/GLiNER) · observed Sep 20, 2026
- Last push (urchade/GLiNER) · observed Aug 10, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 14, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: lightly-train 1.7k · GLiNER 3.5k (synced Sep 20, 2026).
Common questions
- What is the difference between lightly-train and GLiNER?
- lightly-train: All-in-one training for vision models: pretraining, fine-tuning, distillation.. GLiNER: Generalist and Lightweight Model for Named Entity Recognition. See the comparison table for live GitHub stats and shared categories.
- When should I choose lightly-train over GLiNER?
- Choose lightly-train over GLiNER when License: lightly-train is AGPL-3.0, GLiNER is Apache-2.0; Requirements: Min 8 GB RAM; Tags unique to lightly-train: computer-vision, contrastive-learning, deep-learning, depth-estimation; Also covers Computer Vision; Lightly-train is a Python-based framework focused on training vision models including YOLO, ViTs, RT-DETR, and DINOv3, offering comprehensive features like pretraining, fine-tuning, and distillation.
- When should I choose GLiNER over lightly-train?
- Choose GLiNER over lightly-train when License: GLiNER is Apache-2.0, lightly-train is AGPL-3.0; Tags unique to GLiNER: information-extraction, large-language-models, named-entity-recognition, natural language processing; Also covers Data & Retrieval; When you need a lightweight solution for named entity recognition across various languages.
- When should I avoid lightly-train?
- Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.
- When should I avoid GLiNER?
- If high precision in niche specializations like medical terms or rare proper nouns is required In scenarios demanding heavy customization beyond basic named entity recognition capabilities
- Is lightly-train or GLiNER more popular on GitHub?
- GLiNER has more GitHub stars (3,545 vs 1,680). Stars measure visibility, not whether either tool fits your constraints.
- Are lightly-train and GLiNER open source?
- Yes - both are open-source projects on GitHub (lightly-train: AGPL-3.0, GLiNER: Apache-2.0).
- Where can I find alternatives to lightly-train or GLiNER?
- GraphCanon lists graph-backed alternatives at lightly-train alternatives and GLiNER alternatives (lightly-train markdown twin, GLiNER 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, lightly-train or GLiNER?
- lightly-train: Very active. GLiNER: 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 lightly-train and GLiNER?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: lightly-train trust report; GLiNER trust report.