Project description
Check if a model supports multimodal inputs.
pip install mmcheck
Quick start
from mmcheck import check
info = check("google/gemma-4-31B-it")
info.multimodal # True
info.input_modalities # ['text', 'image', 'video']
info.supports("image") # True
info.supports("audio") # False
CLI
mmcheck google/gemma-4-31B-it
# Model: google/gemma-4-31B-it
# Multimodal: YES
# Inputs: text, image, video
# Outputs: text
mmcheck meta-llama/Llama-3-8B
# Multimodal: NO
mmcheck --json google/gemma-4-31B-it
mmcheck --offline gemma-4-31B-it
How it works
Three layers, checked in order:
- Built-in registry — 30+ popular models (GPT-4o, Claude, Gemini, Llama, Qwen). Instant, no network.
- HuggingFace Hub — fetches
config.json, looks forvision_config,audio_encoder, architecture class names. - vLLM cross-reference — tags models with vLLM multimodal support status.
| Modality | Detection |
|---|---|
| Image | vision_config, vision_tower, known VLM architectures |
| Audio | audio_config, audio_encoder, Whisper, Ultravox |
| Video | video_config, LLaVA-Next-Video, MiniCPM-V |
Gated models
For gated HuggingFace models (401/403), mmcheck falls back to the public API metadata (tags, pipeline_tag). If you want full config inspection:
export HF_TOKEN=hf_...
mmcheck google/gemma-4-31B-it
Or in Python:
info = check("google/gemma-4-27b-it", token="hf_...")
License
MIT
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