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A 2B parameter vision-language model fine-tuned from Qwen/Qwen3-VL-2B-Instruct for LaTeX formula recognition from images. This model converts mathematical formula images into accurate LaTeX code.
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Descrição da fonte
A 2B parameter vision-language model fine-tuned from Qwen/Qwen3-VL-2B-Instruct for LaTeX formula recognition from images. This model converts mathematical formula images into accurate LaTeX code.
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1 fonteVerificado 27 de ago.
Artefatos de modelo
1 artefatoTrechos de fonte
2 trechos| Property | Value |
|---|---|
| Base Model | Qwen/Qwen3-VL-2B-Instruct |
| Model Type | Vision-Language Model (Image-to-Text) |
| Parameters | 2B |
| Language | English |
| License | Apache 2.0 |
| Developer | Kassadin88 |
Trained on the LaTeX-OCR dataset for mathematical formula image to LaTeX conversion. The dataset contains rendered LaTeX formulas paired with their source LaTeX code.
| Type | Description |
|---|---|
| Inline formulas | Simple expressions like $E = mc^2$ |
| Display equations | Centered equations with equation numbering |
| Matrices | Matrix and array environments |
| Multi-line expressions | Aligned, gathered, and cases environments |
| Complex formulas | Nested fractions, integrals, summations, and tensor notation |
from transformers import AutoModelForCausalLM, AutoProcessor
from PIL import Image
model_name = "Kassadin88/Qwen3-VL-2B-LaTeX-OCR"
model = AutoModelForCausalLM.from_pretrained(
model_name,
trust_remote_code=True,
torch_dtype="auto"
)
processor = AutoProcessor.from_pretrained(
model_name,
trust_remote_code=True
)
image = Image.open("formula.png")
messages = [
{"role": "user", "content": [
{"type": "image"},
{"type": "text", "text": "Transcribe the formula in the image to LaTeX."}
]}
]
inputs = processor.apply_chat_template(
messages,
images=[image],
return_tensors="pt"
)
outputs = model.generate(**inputs, max_new_tokens=512)
result = processor.decode(outputs[0], skip_special_tokens=True)
print(result)
vllm serve Kassadin88/Qwen3-VL-2B-LaTeX-OCR \
--port 8000 \
--max-model-len 4096 \
--trust-remote-code
messages = [
{"role": "user", "content": [
{"type": "image"},
{"type": "text", "text": "Convert this mathematical formula to LaTeX."}
]}
]
@misc{qwen3-vl-2b-latex-ocr,
author = {Kassadin88},
title = {Qwen3-VL-2B-LaTeX-OCR: A Fine-Tuned Vision-Language Model for LaTeX OCR},
year = {2026},
publisher = {HuggingFace},
url = {https://huggingface.co/Kassadin88/Qwen3-VL-2B-LaTeX-OCR}
}
Note: This model is intended for research and educational purposes. Please use responsibly.
--- license: apache-2.0 language: - en base_model: - Qwen/Qwen3-VL-2B-Instruct pipeline_tag: image-to-text tags: - vision-language-model - latex-ocr - formula-recognition - qwen3-vl - fine-tuned --- # Qwen3-VL-2B-LaTeX-OCR A 2B parameter vision-language model fine-tuned from [Qwen/Qwen3-VL-2B-Instruct](Qwen/Qwen3-VL-2B-Instruct_URL) for **LaTeX formula recognition from images**. This model converts mathematical formula images into accurate LaTeX code. ## Model Highlights - **Specialized for LaTeX OCR**: Trained to accurately transcribe mathematical formulas from images to LaTeX - **Multi-Format Support**: Handles inline formulas, display equations, matrices, and complex multi-line expressions - **High Accuracy**: Significantly improved formula recognition over the base model - **Vision-Language Architecture**: Leverages Qwen3-VL's visual understanding capabilities ## Model Description | Property | Value | |----------|-------| | **Base Model** | Qwen/Qwen3-VL-2B-Instruct | | **Model Type** | Vision-Language Model (Image-to-Text) | | **Parameters** | 2B | | **Language** | English | | **License** | Apache 2.0 | | **Developer** | [Kassadin88](https://huggingface.co/Kassadin88) | ## Training Data Trained on the [LaTeX-OCR dataset](https://huggingface.co/datasets/linxy/LaTeX_OCR) for mathematical formula image to LaTeX conversion. The dataset contains rendered LaTeX formulas paired with their source LaTeX code. ### Data Composition | Type | Description | |------|-------------| | **Inline formulas** | Simple expressions like $E = mc^2$ | | **Display equations** | Centered equations with equation numbering | | **Matrices** | Matrix and array environments | | **Multi-line expressions** | Aligned, gathered, and cases environments | | **Complex formulas** | Nested fractions, integrals, summations, and tensor notation | ## Quick Start ### Using Transformers ```python from transformers import AutoModelForCausalLM, AutoProcessor from PIL import Image model_name = "Kassadin88/Qwen3-VL-2B-LaTeX-OCR" model = AutoModelForCausalLM.from_pretrained( model_name, trust_remote_code=True, torch_dtype="auto" ) processor = AutoProcessor.from_pretrained( model_name, trust_remote_code=True ) image = Image.open("formula.png") messages = [ {"role": "user", "content": [ {"type": "image"}, {"type": "text", "text": "Transcribe the formula in the image to LaTeX."}...
Source context: 9 downloads · 0 likes · Pipeline image-to-text · Repo clzoro/Qwen3-VL-2B-LaTeX-OCR