Model card The label on the model: what it is for, how it was tested, where it fails.
Short answer. A model card is a short document published with a trained AI model that says what the model is for, how it was built and tested, and where it should not be used. The idea comes from a 2018 paper, “Model Cards for Model Reporting”, which proposed reporting performance across different conditions and groups. On Hugging Face the card is the repository’s README file, with a metadata block at the top for the licence, languages, datasets and base model. Read it before you download anything: it tells you the licence, the context length, the training stage and the maker’s recommended settings.
01
What a card contains
Hugging Face’s documentation says a card should describe the model, its intended uses and limitations including biases, its training parameters, the datasets used and its evaluation results. The right-hand column shows what published cards actually state.
Section
What to look for
Example from a published card
Model details
Size, architecture, context length, training stage
Qwen3-32B: 32.8B parameters, 64 layers, 32,768 tokens natively and 131,072 with YaRN
Intended use
Tasks, languages, users
Llama 3.1: commercial and research use in multiple languages; instruction-tuned versions for assistant-like chat
DeepSeek-R1-Distill-Qwen-32B: AIME 2024 pass@1 of 72.6 and MATH-500 of 94.3
Licence
What you may do with it
Apache 2.0, the Llama 3.1 Community License, or the Gemma terms
Usage guidance
Recommended settings and prompt format
Qwen3: temperature 0.6 and top-p 0.95 in thinking mode. DeepSeek: put instructions in the user prompt
02
A checklist for an office
Licence: does it allow your use, including any acceptable-use policy it points to?
Training stage: base or instruction-tuned? You almost always want the tuned one for an assistant.
Context length: the model’s maximum, and what your runtime will actually allow.
Languages: is Hindi listed, and has anyone tested Hinglish or mixed text?
Size and formats: how much memory the files need at the precision you will use.
Settings: the maker’s recommended temperature, top-p and prompt format.
Evidence: results on tasks like yours, not only headline benchmarks.
Provenance: published by the maker’s own organisation, with a date and version.
03
What a card does not tell you
Whether the model works on your documents. Benchmark scores are measured by the publisher on public tests, so run your own.
The full training data. Cards usually summarise it, while the Open Source AI Definition asks for sufficiently detailed information about it.
That the card is complete or correct. Cards can be thin, out of date, or copied onto a re-upload by someone else.
That the files you downloaded are the ones the card describes. Compare the checksum.
04
Keeping your own record
Treat the card as the start of an internal record. For each model in use, note its name and exact version, the source address, the licence, the date you checked it, and the settings you chose. A system that logs the model with every request makes later questions easy to answer: ANVE’s model gateway logs each call with the user, the app and the model, and whether it ran locally or in the cloud.
05
Questions people ask
Who writes the model card?
The publisher of the model, usually the lab or the person who released the weights. Re-uploads and quantised copies often carry a shortened or copied card, so go back to the original.
Where do I find the model card?
On Hugging Face it is the README of the model repository, shown on the model’s main page. Other sources, such as Meta’s Llama repository, publish a model card file alongside the code.
Does a model card prove the model is safe?
No. It documents what the publisher chose to report. Test the model on your own tasks and review its licence and limits.
What is in the metadata block at the top?
Fields such as the licence, languages, datasets, base model and library. The Hub uses them for filters and to display the licence on the model page.
06
What is not real yet
No sealed ANVE unit exists; every image of the appliance on this site is a labelled render. The software runs today on a development machine. No customers yet, no certifications, no delivery date, and the final price is not announced.
Reservations are ₹999 in India or US$12 elsewhere, refundable in full until you confirm an order in writing (terms). Offices that cannot use cloud AI can apply to be one of ten design partners.
