---
license: mit
library_name: onnx
tags:
  - minicheck
  - onnx
  - fact-checking
  - text2text-generation
  - flan-t5
  - natural-language-inference
base_model: lytang/MiniCheck-Flan-T5-Large
pipeline_tag: text-classification
---

# MiniCheck-Flan-T5-Large (ONNX)

An ONNX export of [**MiniCheck-Flan-T5-Large**](https://huggingface.co/lytang/MiniCheck-Flan-T5-Large)
(770M, FLAN-T5-large fine-tuned for grounded fact-checking), for in-process
inference without a Python/PyTorch runtime.

## Why this exists

Upstream MiniCheck publishes only the PyTorch checkpoint - no ONNX build existed
for this 770M encoder-decoder verifier. This is that build, so anyone who wants
to experiment with MiniCheck-Flan-T5-Large can, without standing up a PyTorch
runtime or writing a custom export pathway.

It reflects a Familiar Tools belief: a specialized, right-sized model that runs
efficiently and in-process beats reaching for a large, general, resource-hungry
one. Exporting a focused model to ONNX is part of that - it makes the model
cheap to run, easy to embed, and light on dependencies. Custom, deliberately
engineered solutions tend to be more efficient and more resource-aware than
general-purpose defaults.

## Files

Exported with `optimum-cli export onnx --task text2text-generation --opset 14 --dtype fp32`.

| File | Notes |
|------|-------|
| `encoder_model.onnx` (~1.3 GB) | Encoder stack. Inputs: `input_ids`, `attention_mask`. |
| `decoder_model.onnx` (~1.8 GB) | Decoder stack. Inputs: `input_ids`, `encoder_hidden_states`, `encoder_attention_mask`. No KV-cache (single decode step). |
| `tokenizer.json` | HF fast tokenizer (loads with the Rust `tokenizers` crate). |
| `spiece.model` | SentencePiece model. |
| `config.json`, `generation_config.json`, `tokenizer_config.json`, `special_tokens_map.json` | T5 config + tokenizer metadata. |

MiniCheck is used as a single-step seq2seq verifier: the encoder reads the
`(document, claim)` prompt, the decoder takes one step, and the probability mass
on the "Yes" vs "No" vocabulary tokens gives the support score.

## License and attribution

Released under the **MIT License**, matching upstream.

- MiniCheck: Tang et al., *MiniCheck: Efficient Fact-Checking of LLMs on
  Grounding Documents*, EMNLP 2024
  ([arXiv:2404.10774](https://arxiv.org/abs/2404.10774)).
- Original weights: [`lytang/MiniCheck-Flan-T5-Large`](https://huggingface.co/lytang/MiniCheck-Flan-T5-Large).
- Base model: `google/flan-t5-large`.

This repo redistributes a derivative (ONNX export) of the above under the same
MIT terms. Weights were not retrained or modified; only the inference graph was
re-expressed.