Open LLMs
RepositoryFreeA list of open LLMs available for commercial use.
Capabilities5 decomposed
curated-open-llm-discovery-and-filtering
Medium confidenceMaintains a continuously updated, manually curated registry of open-source large language models with commercial-use licensing. The repository implements a structured catalog approach where each model entry includes metadata (model name, organization, parameter count, license type, release date, and commercial eligibility) organized in markdown tables and JSON structures, enabling developers to filter and discover models based on licensing constraints, model size, and use-case suitability without legal ambiguity.
Focuses specifically on commercial-use licensing eligibility rather than general model benchmarking or capability comparison — filters out models with restrictive licenses (e.g., research-only, non-commercial clauses) upfront, reducing legal risk for production deployments
More legally-focused than Hugging Face Model Hub (which lists all models regardless of commercial restrictions) and more current than static LLM comparison papers, providing a practical filtering layer for compliance-conscious teams
model-metadata-aggregation-and-normalization
Medium confidenceAggregates heterogeneous model metadata from multiple sources (model cards, GitHub repositories, research papers, official announcements) and normalizes it into a consistent schema with fields for model name, organization, parameter count, license, release date, and commercial-use status. The implementation uses markdown tables as the primary data structure with optional JSON exports, enabling both human-readable browsing and programmatic access through simple parsing.
Uses a deliberately simple, human-readable markdown-first schema rather than complex database structures, making the registry accessible to non-technical stakeholders while remaining machine-parseable for automation
Simpler and more accessible than database-backed model registries (e.g., MLflow Model Registry) but less queryable; trades flexibility for transparency and ease of contribution
commercial-license-eligibility-filtering
Medium confidenceImplements a filtering mechanism that categorizes models by their license type and commercial-use permissions, distinguishing between fully commercial-eligible models (Apache 2.0, MIT, OpenRAIL-M) and restricted models (research-only, non-commercial clauses, or ambiguous licensing). The filtering is applied at the curation stage where models are manually reviewed against licensing criteria before inclusion in the registry.
Explicitly prioritizes commercial-use licensing as the primary filtering criterion rather than model performance or capability, addressing a specific pain point for enterprises that need legal certainty before deployment
More legally-focused than general model discovery tools; provides clearer commercial-use guidance than raw license documents, though less authoritative than legal counsel
open-source-model-ecosystem-tracking
Medium confidenceMaintains a longitudinal view of the open-source LLM ecosystem by tracking model releases, organizational contributions, licensing trends, and parameter-size distributions over time. The repository serves as a historical record of which organizations are releasing open models, when they were released, and how the landscape has evolved, enabling analysis of ecosystem maturity and competitive dynamics.
Provides a curated, human-reviewed historical record of open-source LLM releases with explicit commercial-use filtering, rather than automated scraping of all models, enabling cleaner trend analysis and reducing noise from research-only or restricted models
More selective and legally-focused than raw Hugging Face statistics; provides organizational and licensing context that raw model counts lack, though less comprehensive than exhaustive ecosystem surveys
model-selection-decision-support
Medium confidenceProvides structured information to support model selection decisions by presenting models in a filterable, comparable format with key decision criteria (license, parameter count, organization, release date). The registry enables side-by-side comparison of models and helps developers quickly narrow down options based on their specific constraints (budget, licensing requirements, model size, organizational preference).
Focuses on commercial-use licensing as a primary decision criterion alongside technical attributes, addressing the specific decision-making needs of enterprises and startups that cannot use restricted models
More legally-aware than generic model comparison tools; provides clearer filtering for commercial use cases, though less comprehensive than full benchmarking suites that include performance metrics
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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Best For
- ✓Startups and enterprises building proprietary LLM applications without budget for commercial API licenses
- ✓Open-source project maintainers seeking compliant base models for derivative works
- ✓ML engineers evaluating the open-source LLM landscape for cost-effective deployment strategies
- ✓Legal/compliance teams vetting model licenses before production deployment
- ✓Developers building model selection tools or LLM comparison dashboards
- ✓Data engineers creating ETL pipelines that need to validate model licensing
- ✓Researchers analyzing trends in open-source LLM releases and organizational contributions
- ✓DevOps teams automating model deployment decisions based on licensing criteria
Known Limitations
- ⚠Curation is manual and subjective — licensing interpretation may lag actual license updates or legal precedent changes
- ⚠No automated license verification or legal validation — relies on human review of license documents
- ⚠Does not track model performance benchmarks, inference speed, or hardware requirements — only metadata
- ⚠Snapshot-based approach means model availability and license status may become stale between updates
- ⚠No integration with model hosting platforms (Hugging Face, ModelScope) for real-time availability checks
- ⚠Metadata schema is fixed and may not capture domain-specific attributes (e.g., multilingual support, instruction-tuning approach, quantization availability)
Requirements
Input / Output
UnfragileRank
UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.
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A list of open LLMs available for commercial use.
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