Image-to-Text
Transformers
ONNX
Safetensors
vision-encoder-decoder
image-text-to-text
typst
math-ocr
formula-recognition
browser
Instructions to use dbcccc/TypLens with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dbcccc/TypLens with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("image-to-text", model="dbcccc/TypLens")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("dbcccc/TypLens") model = AutoModelForMultimodalLM.from_pretrained("dbcccc/TypLens", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| # License and attribution | |
| ## Released weights and metadata | |
| TypLens-V1 is a fine-tuned derivative of Pix2Text-MFR-1.5, not the original | |
| random-initialized IBEM-im2typst Phase10 model. The FP32 and INT8 exports are | |
| variants of the same fine-tuned checkpoint. Copyright (c) 2026 dbcccc. | |
| The project contributions to these weights and metadata are licensed under the | |
| MIT License, to the extent the contributors hold applicable licensable rights. | |
| Upstream rights and notices are retained. Keep LICENSE, this notice, and the | |
| licenses directory with redistributed model files. The MIT grant does not | |
| require publication of training code. No training source is included. | |
| ## Pretrained parameters | |
| - Pix2Text-MFR-1.5 by BreezeDeus: [model card](https://huggingface.co/breezedeus/pix2text-mfr-1.5), | |
| [pinned source revision](https://huggingface.co/breezedeus/pix2text-mfr-1.5/tree/1cef9f0bdcd6a4c63df7de1311fb0894593340cc). | |
| The pinned model metadata declares MIT. The associated project's notice is | |
| Copyright (c) 2022 BreezeDeus; retained in licenses/PIX2TEXT-MIT.txt. | |
| [Upstream license](https://github.com/breezedeus/Pix2Text/blob/main/LICENSE). | |
| - The upstream model card attributes its initialization and architecture to | |
| Microsoft's TrOCR. The corresponding Microsoft MIT notice is retained in | |
| licenses/TROCR-MIT.txt. | |
| [TrOCR project](https://github.com/microsoft/unilm/tree/master/trocr), | |
| [license](https://github.com/microsoft/unilm/blob/master/LICENSE). | |
| Changes include native Typst output targets and vocabulary, fine-tuning, screenshot | |
| augmentation, a cached ONNX decoder export, and an optional INT8 weight export. | |
| ## Training-data attribution (datasets are not redistributed) | |
| - **IBEM**, Dan Anitei, Joan Andreu Sanchez, and Jose Miguel Benedi: | |
| [Zenodo record 7963703](https://zenodo.org/records/7963703), | |
| [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). | |
| Formula crops and annotations were used to derive native Typst supervision. | |
| - **UniMER Dataset**, Bin Wang, Zhuangcheng Gu, Chao Xu, Bo Zhang, Botian Shi, | |
| and Conghui He: [dataset card](https://huggingface.co/datasets/wanderkid/UniMER_Dataset), | |
| source revision 2343ddd963290469da36ca83e3a56c66e068add9. | |
| The dataset card declares Apache-2.0 and identifies component sources including | |
| Pix2tex, arXiv, CROHME and HME100K. It also gives a separate copyright-related | |
| download instruction for HME100K. The selected training images were not all | |
| traced to component-level rights. This release does not represent that every | |
| component image has a uniform license or provide a separate commercial-rights | |
| clearance for those source materials. | |
| No dataset images, original annotations, or source PDFs are included. The model | |
| license does not relicense those materials. Runtime libraries, Typst, MiTeX, and | |
| ONNX Runtime binaries are also not included and retain their own licenses. | |