{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-open-llms","slug":"open-llms","name":"Open LLMs","type":"repo","url":"https://github.com/eugeneyan/open-llms","page_url":"https://unfragile.ai/open-llms","categories":["model-training"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-open-llms__cap_0","uri":"capability://search.retrieval.curated.open.llm.discovery.and.filtering","name":"curated-open-llm-discovery-and-filtering","description":"Maintains 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.","intents":["Find open-source LLMs that are legally safe for commercial deployment without licensing restrictions","Compare model capabilities and sizes across the open-source ecosystem to select appropriate base models for fine-tuning","Identify newly released open models that meet specific commercial licensing requirements","Understand which models have clear commercial-use permissions vs those with restrictions"],"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"],"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"],"requires":["GitHub account to access the repository","Basic understanding of open-source licensing (Apache 2.0, MIT, OpenRAIL, etc.)","No API keys or runtime dependencies required"],"input_types":["text (filtering criteria: license type, model size, organization)","structured queries (parameter count ranges, release date filters)"],"output_types":["markdown tables with model metadata","JSON structured data with model records","filtered lists of models matching commercial-use criteria"],"categories":["search-retrieval","knowledge-base"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-open-llms__cap_1","uri":"capability://data.processing.analysis.model.metadata.aggregation.and.normalization","name":"model-metadata-aggregation-and-normalization","description":"Aggregates 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.","intents":["Quickly look up standardized information about a specific open LLM without visiting multiple sources","Export model metadata in structured formats for downstream tooling or analysis","Track model release timeline and organizational trends in open-source LLM development","Build automated tools that consume the model registry for license compliance checking"],"best_for":["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"],"limitations":["Metadata schema is fixed and may not capture domain-specific attributes (e.g., multilingual support, instruction-tuning approach, quantization availability)","No versioning system for model metadata — updates overwrite previous records without history","Manual data entry is error-prone and doesn't scale to hundreds of models without automation","Markdown tables are not ideal for complex queries or large-scale filtering operations","No validation layer to ensure metadata accuracy or consistency across entries"],"requires":["GitHub repository access to clone or download the metadata","Basic markdown parsing capability or JSON export functionality","No authentication or API keys required"],"input_types":["structured metadata fields (model name, org, parameters, license, date)","markdown table format or JSON objects"],"output_types":["normalized JSON objects with consistent schema","markdown tables for human consumption","CSV exports for spreadsheet analysis"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-open-llms__cap_2","uri":"capability://safety.moderation.commercial.license.eligibility.filtering","name":"commercial-license-eligibility-filtering","description":"Implements 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.","intents":["Identify which open models can be legally used in commercial products without licensing fees or restrictions","Avoid legal liability by excluding models with non-commercial or research-only licenses","Understand the licensing landscape and which organizations are releasing commercially-friendly models","Make informed decisions about model selection based on business requirements and legal constraints"],"best_for":["Legal and compliance teams evaluating open-source model adoption","Product managers deciding between open-source and commercial LLM APIs","Startups with limited budgets seeking cost-effective, legally-safe model options","Enterprise architects planning internal LLM infrastructure with clear licensing requirements"],"limitations":["License interpretation is subjective and may not reflect actual legal enforceability in all jurisdictions","Does not provide legal advice or guarantee that models are truly safe for commercial use — requires legal review","Licensing landscape changes frequently; registry may lag behind license updates or new legal interpretations","No automated license verification — relies entirely on manual curation and human judgment","Does not account for indirect licensing issues (e.g., training data licensing, derivative work restrictions)"],"requires":["Understanding of open-source licenses (Apache 2.0, MIT, OpenRAIL, etc.)","Legal review capability or access to legal counsel for final compliance decisions","No technical dependencies or API keys"],"input_types":["license type (text field)","commercial-use eligibility flag (boolean or categorical)"],"output_types":["filtered model lists by license category","binary eligibility status (commercial-safe vs restricted)","license type groupings for analysis"],"categories":["safety-moderation","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-open-llms__cap_3","uri":"capability://data.processing.analysis.open.source.model.ecosystem.tracking","name":"open-source-model-ecosystem-tracking","description":"Maintains 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.","intents":["Understand which organizations are leading open-source LLM development and their licensing philosophies","Track the growth and maturation of the open-source LLM ecosystem over time","Identify gaps in the open-source landscape (e.g., underserved model sizes or domains)","Analyze trends in model parameter counts and licensing strategies across the industry"],"best_for":["Researchers studying the open-source AI ecosystem and organizational dynamics","Investors evaluating the competitive landscape and open-source model adoption trends","Product strategists planning model development roadmaps based on ecosystem gaps","Community organizers and open-source advocates tracking progress in democratizing LLMs"],"limitations":["Historical data is limited to the repository's creation date — no retroactive data for models released before curation began","No quantitative performance metrics or capability benchmarks — only metadata and release information","Curation bias may favor well-known organizations and models, underrepresenting smaller or regional projects","No automated data collection — relies on manual updates and community contributions","Does not track model deprecation, discontinuation, or license changes after initial release"],"requires":["GitHub repository access to view historical commits and updates","Basic understanding of the LLM landscape and organizational players","No API keys or technical dependencies"],"input_types":["model release metadata (name, org, date, parameters, license)","historical snapshots from repository commits"],"output_types":["timeline visualizations of model releases","organizational contribution summaries","trend analysis of parameter counts and licensing strategies","ecosystem maturity assessments"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-open-llms__cap_4","uri":"capability://planning.reasoning.model.selection.decision.support","name":"model-selection-decision-support","description":"Provides 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).","intents":["Choose between multiple open-source models that meet licensing requirements","Compare models by size, organization, and release date to make informed selection decisions","Quickly identify the best-fit model for a specific use case given commercial and technical constraints","Justify model selection decisions to stakeholders with clear, documented criteria"],"best_for":["ML engineers and architects selecting base models for fine-tuning or deployment","Product managers evaluating open-source vs commercial LLM options","Teams with specific constraints (budget, licensing, model size) seeking optimal model selection","Developers prototyping LLM applications and needing quick model evaluation"],"limitations":["Does not include performance benchmarks or capability comparisons — only metadata","No integration with model hosting platforms for real-time availability or download statistics","Filtering is manual (reading and comparing) rather than programmatic — no query API","Does not account for downstream factors like fine-tuning difficulty, inference cost, or community support","No personalization or recommendation engine — all users see the same static registry"],"requires":["Access to the GitHub repository","Basic understanding of model parameters and licensing","No API keys or technical setup required"],"input_types":["decision criteria (license type, parameter count range, organization, release date)","use-case requirements (commercial use, specific domain, model size)"],"output_types":["filtered model lists matching criteria","side-by-side model comparisons","decision matrices with key attributes","model recommendations based on 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