FamiliarTools/MiniCheck-Flan-T5-Large-onnx

MiniCheck-Flan-T5-Large (ONNX)

An ONNX export of 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).
• Original weights: 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.