AI Space

AI Models & Assistants / Knowledge library

Model cards

Document model purpose, license, limitations and evaluation context.

AI Workbench · sourced catalog

Why learn Model cards?

Make deployment decisions traceable to model-specific evidence.

The problem it solves

Reduce unsupported assumptions about model capabilities.

Where it came from

Model cards: Document model purpose, license, limitations and evaluation context.

Build your understanding

Work through these exercises in order. Try the task before opening a reference.

  1. Explain what Model cards does.
  2. Identify a use case for Model cards.
  3. Describe a limitation of Model cards.
  4. Name a compatible tool for Model cards.
  5. Compare Model cards against an alternative.

Documentation and sources

Catalog research notes

Connect the ideas

Built on

Model Context Protocol (workbench)LLM (Large Language Model)

Enables

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1GPT-Live-Transcribegpt-oss-120bgpt-oss-20bGPT-RealtimeGPT-Realtime-1.5GPT-Realtime-2GPT-Realtime-2.1GPT-Realtime-2.1 MiniGPT-Realtime MiniGPT-Realtime-TranslateGPT-Realtime-WhisperGPT-Transcribeo1o1-minio1 Previewo1-proo3o3-deep-researcho3-minio3-proo4-minio4-mini-deep-researchomni-moderationtext-embedding-3-largetext-embedding-3-smalltext-embedding-ada-002text-moderationtext-moderation-stableTTS-1TTS-1 HDWhisperGPT-1GPT-2GPT-3Claude Fable 5.1Claude Mythos 5.1Claude Fable 5Claude Mythos 5Claude Mythos PreviewClaude Opus 5.5Claude Opus 5Claude Opus 4.8Claude Opus 4.7Claude Opus 4.6Claude Opus 4.5Claude Opus 4.1Claude Opus 4Claude Sonnet 5.5Claude Sonnet 5Claude Sonnet 4.6Claude Sonnet 4.5Claude Sonnet 4Claude 3.7 SonnetClaude Haiku 5.5Claude Haiku 4.5Claude 3.5 HaikuClaude 3 HaikuClaude 3.5 SonnetClaude 3.5 Sonnet (claude 3 5 sonnet 20241022)Claude 3 OpusClaude 2.0Claude 2.1Claude 3 SonnetClaude 1.0Claude 1.1Claude 1.2Claude 1.3Claude Instant 1.0Claude Instant 1.1Claude Instant 1.2gemini 3.8 flashgemini 3.8 livegemini 3.8 live extended thinkinggemini 3.8 flash ttsgemini 3.8 flash lite ttsgemini 3.6 flashgemini 3.5 flash litegemini 3.1 flash litegemini nano banana 2.1gemini 3.1 flash imagegemini 3.1 flash lite imagegemini 3 pro imagegemini 3.1 pro previewgemini 3 flash previewgemini 3.5 live translate previewgemini 3.1 flash live previewgemini 3.1 flash tts previewgemini omni 1.1 flashgemini 2.5 flashgemini 2.5 flash imagegemini 2.5 flash native audio preview 12 2025gemini 2.5 flash preview ttsgemini 2.5 flash litegemini 2.5 progemini 2.5 pro preview ttsgemini embedding 2 previewgemini embedding 001gemini robotics er 2 previewgemini robotics er 1.6 previewgemini 2.5 computer use preview 10 2025gemini 2.0 flashgemini 2.0 flash litegemini 3.1 flash lite previewgemini 3 pro previewgemini 2.5 flash preview 09 2025gemini flash latestPaLMPaLM 2Gemini 1.0Gemini 1.5 ProGrok-1grok-4.7grok-4.6grok-4.5grok-4.3grok-4.20-0309-reasoninggrok-4.20-0309-non-reasoninggrok-build-0.1grok-4.20-multi-agent-0309DeepSeek V4.1 FlashDeepSeek V4 ProQwen3-0.6Bgpt2 (openai community gpt2)Qwen3-4BQwen3-8BQwen2.5-7B-InstructLlama-3.2-1B-InstructQwen2.5-0.5B-InstructQwen2.5-1.5B-InstructQwen3-14Bopt-125mtiny-Qwen2ForCausalLM-2.5gpt-oss-20b (openai gpt oss 20b)Llama-3.1-8B-InstructKimi-K3-DSparkOrnith-1.5-9B-GGUFQwen3.6-35B-A3B-NVFP4DeepSeek-V3.2dolphin-2.9.1-yi-1.5-34bTernary-Bonsai-2-27B-ggufDeepSeek-V4-Flash-0731Qwen2.5-3B-InstructQwen3-Coder-30B-A3B-Instruct-GGUFQwen3-1.7BOrnith-1.5-35B-A3B-GGUFgpt-oss-120b (openai gpt oss 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gguf)Wan2.1-T2V-1.3B-DiffusersMiniMax-H3-Turbo-LoraMiniMax-H3-Turbo-Lora-ComfyUIWan_2.2_ComfyUI_RepackagedLTX-2.5Minimax-h3-TurboLTX-2.3wav2vec2-large-xlsr-53-japanesewhisperkit-coremlspeaker-diarization-3.1whisper-large-v3-turbospeaker-diarization-community-1wav2vec2-large-xlsr-53-portugueseKokoro-82MXTTS-v2audio.cpp-ggufQwen3-TTS-12Hz-1.7B-CustomVoicechatterboxOmniVoiceall-MiniLM-L6-v2paraphrase-multilingual-MiniLM-L12-v2bge-m3all-mpnet-base-v2nomic-embed-text-v1.5multilingual-e5-smallbge-small-en-v1.5bge-large-en-v1.5bge-base-en-v1.5Qwen3-Embedding-0.6BGLM-OCRblip-image-captioning-basemanga-ocr-baselocate-anything.cpp-gguftable-transformer-structure-recognitionrtdetr_r101vd_coco_o365Qwen3-Coder-480B-A35B-InstructQwen3-Coder-30B-A3B-InstructQwen2.5-Coder-32B-InstructDeepSeek-R1-0528Qwen3-235B-A22B-Instruct-2507Qwen3-235B-A22B-Thinking-2507Devstral-Small-2505Mistral-Small-3.2-24B-Instruct-2506Llama-3.3-70B-Instructgemma-3-27b-itDeepSeek-R1-Distill-Qwen-32Bsqlcoder-7b-2medgemma-27b-text-itmedgemma-4b-itlaw-chatfinance-chat

Real World Example

Illustrative business scenario · AI-generated from linked documentation · Not a measured customer case study

Standardizing Model Governance for Enterprise Compliance

Enterprise AI teams and compliance officers

The business problem

Internal teams struggle to track model lineage, licensing, and intended use cases, leading to unauthorized deployments and regulatory risk.

How this topic solves it

Implementing mandatory Model Cards as YAML-based metadata headers in model repositories to enforce discoverability, license transparency, and evaluation context.

  1. Define a standard YAML template including license, base_model, and pipeline_tag fields.
  2. Integrate the metadata update process into the CI/CD pipeline for model uploads.
  3. Populate the model-index section with structured evaluation results for auditability.
  4. Review the human-readable Markdown section for ethical considerations and limitations.
Model Card Governance Workflow · Process flow

The flow from model development to compliant repository hosting.

Model Developer

Creates model and documentation

YAML Metadata

Structured license and task data

Markdown Body

Human-readable limitations and context

Model Hub

Centralized repository hosting

Compliance Audit

Automated verification of metadata

  • Model Developer → defines → YAML Metadata
  • Model Developer → writes → Markdown Body
  • YAML Metadata → configures → Model Hub
  • Markdown Body → documents → Model Hub
  • Model Hub → enables → Compliance Audit

Expected business outcome

Improved model discoverability and automated compliance auditing across the organization.

Limitations and checks

Metadata is static and does not automatically update when underlying model weights or training datasets change; manual verification is required to ensure the card remains accurate.

Sources

Generated 2026-10-18 with gemini-3.1-flash-lite. Your assessment includes two questions about this example.

Catalog source: technology-maxxing. This page reproduces the existing catalog’s learning material and source links.