| --- |
| 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. |
|
|
