Comparison
DeepSeek-R1 vs automl-gs
Verdict
Pick DeepSeek-R1 when pricing: The repository allows for commercial use under the MIT License or respective original licenses with no explicit monetary costs outlined in the repository.; pick automl-gs when tags unique to automl-gs: automl, keras, machine-learning, python.
Markdown twin · DeepSeek-R1 alternatives · automl-gs alternatives
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Trust & integrity
| Signal | DeepSeek-R1 | automl-gs |
|---|---|---|
| Maintenance | Dormant (379d since push) As of today · github_public_v1 | Dormant (2454d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Organization account As of today · github_public_v1 | Not a fork · Personal account As of today · github_public_v1 |
| Security (OSV) | No lockfile As of 1d · none | 2 high, 5 medium, 7 low (2 high, 5 medium, 7 low) As of today · osv@v1 |
Tagline
- DeepSeek-R1
- Repository contains distilled LLM models derived from Qwen and LLaMA series for various commercial uses.
- automl-gs
- Provide an input CSV and a target field to predict, generate a model + code to run it.
Stars
- DeepSeek-R1
- 92k
- automl-gs
- 1.9k
Forks
- DeepSeek-R1
- 12k
- automl-gs
- 181
Open issues
- DeepSeek-R1
- 45
- automl-gs
- 28
Language
- DeepSeek-R1
- -
- automl-gs
- Python
Adopt for
- DeepSeek-R1
- DeepSeek-R1 provides a set of distilled LLMs from Qwen and LLaMA series that support commercial use.
- automl-gs
- -
Persona
- DeepSeek-R1
- -
- automl-gs
- -
Runtime
- DeepSeek-R1
- -
- automl-gs
- -
License
- DeepSeek-R1
- MIT
- automl-gs
- MIT
Last pushed
- DeepSeek-R1
- Jun 27, 2025
- automl-gs
- Oct 22, 2019
Categories
- DeepSeek-R1
- LLM Frameworks, Model Training
- automl-gs
- Model Training
Trust and health
Days since push
- DeepSeek-R1
- 379d
- automl-gs
- 2454d
Open issues (now)
- DeepSeek-R1
- 45
- automl-gs
- 28
Owner type
- DeepSeek-R1
- Organization
- automl-gs
- User
Security scan
- DeepSeek-R1
- No lockfile
- automl-gs
- 2 high, 5 medium, 7 low (2 high, 5 medium, 7 low)
Full report
- DeepSeek-R1
- Trust report
- automl-gs
- Trust report
Choose DeepSeek-R1 if…
- Pricing: The repository allows for commercial use under the MIT License or respective original licenses with no explicit monetary costs outlined in the repository..
- Requirements: Min 4 GB RAM; This is a rough estimate based on common model requirements. Specific models within DeepSeek-R1 may have different resource needs..
- Tags unique to DeepSeek-R1: commercial use, derived models, distilled models, mit license.
- Also covers LLM Frameworks.
- When you need to work with pre-trained models derived specifically from the Qwen-2.5 and Llama3.x series, benefiting from their unique characteristics.
When NOT to use DeepSeek-R1
- Avoid if you need foundational models rather than distilled versions, as DeepSeek-R1 specializes in providing smaller, more efficient models suitable for resource-constrained environments.
- If your project is tightly regulated or requires models from a different lineage, as DeepSeek-R1 exclusively provides derivatives of Qwen and LLaMA series.
Choose automl-gs if…
- Tags unique to automl-gs: automl, keras, machine-learning, python.
- Leaner open-issue backlog (28).
When NOT to use automl-gs
- Last GitHub push was 2454 days ago (dormant maintenance, Oct 22, 2019). Validate activity before betting a new project on automl-gs.
- 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 (deepseek-ai/DeepSeek-R1) · observed Jul 12, 2026
- GitHub forks (deepseek-ai/DeepSeek-R1) · observed Jul 12, 2026
- Last push (deepseek-ai/DeepSeek-R1) · observed Jun 27, 2025
- License file (MIT) · observed Jul 12, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (minimaxir/automl-gs) · observed Jul 11, 2026
- GitHub forks (minimaxir/automl-gs) · observed Jul 11, 2026
- Last push (minimaxir/automl-gs) · observed Oct 22, 2019
- License file (MIT) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: DeepSeek-R1 92k · automl-gs 1.9k (synced Jul 12, 2026).
Common questions
- What is the difference between DeepSeek-R1 and automl-gs?
- DeepSeek-R1: Repository contains distilled LLM models derived from Qwen and LLaMA series for various commercial uses.. automl-gs: Provide an input CSV and a target field to predict, generate a model + code to run it.. See the comparison table for live GitHub stats and shared categories.
- When should I choose DeepSeek-R1 over automl-gs?
- Choose DeepSeek-R1 over automl-gs when Pricing: The repository allows for commercial use under the MIT License or respective original licenses with no explicit monetary costs outlined in the repository.; Requirements: Min 4 GB RAM; This is a rough estimate based on common model requirements. Specific models within DeepSeek-R1 may have different resource needs.; Tags unique to DeepSeek-R1: commercial use, derived models, distilled models, mit license; Also covers LLM Frameworks; When you need to work with pre-trained models derived specifically from the Qwen-2.5 and Llama3.x series, benefiting from their unique characteristics.
- When should I choose automl-gs over DeepSeek-R1?
- Choose automl-gs over DeepSeek-R1 when Tags unique to automl-gs: automl, keras, machine-learning, python; Leaner open-issue backlog (28).
- When should I avoid DeepSeek-R1?
- Avoid if you need foundational models rather than distilled versions, as DeepSeek-R1 specializes in providing smaller, more efficient models suitable for resource-constrained environments. If your project is tightly regulated or requires models from a different lineage, as DeepSeek-R1 exclusively provides derivatives of Qwen and LLaMA series.
- When should I avoid automl-gs?
- Last GitHub push was 2454 days ago (dormant maintenance, Oct 22, 2019). Validate activity before betting a new project on automl-gs. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.
- Is DeepSeek-R1 or automl-gs more popular on GitHub?
- DeepSeek-R1 has more GitHub stars (91,991 vs 1,866). Stars measure visibility, not whether either tool fits your constraints.
- Are DeepSeek-R1 and automl-gs open source?
- Yes - both are open-source projects on GitHub (DeepSeek-R1: MIT, automl-gs: MIT).
- Where can I find alternatives to DeepSeek-R1 or automl-gs?
- GraphCanon lists graph-backed alternatives at DeepSeek-R1 alternatives and automl-gs alternatives (DeepSeek-R1 markdown twin, automl-gs 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, DeepSeek-R1 or automl-gs?
- DeepSeek-R1: Dormant. automl-gs: 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 DeepSeek-R1 and automl-gs?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: DeepSeek-R1 trust report; automl-gs trust report.