Mustafaege/qwen3.5-vision-ocr-v2

Qwen3.5 Vision OCR Dataset v2

An expanded LaTeX OCR dataset for Qwen3.5-VL fine-tuning, combining unsloth/LaTeX_OCR (1% sample) and the full linxy/LaTeX_OCR dataset. Provides 2x more coverage including printed and handwritten formulas, all in Qwen3-VL multimodal messages format.

Dataset Summary

Property	Value
Total Samples	~145K
Train Split	~130K
Test Split	~15K
Sources	unsloth/LaTeX_OCR + linxy/LaTeX_OCR (full)
Format	Qwen3-VL multimodal messages
Task	Image → LaTeX formula
License	Apache 2.0

v1 vs v2 Comparison

Version	Samples	Coverage	Sources
v1	68,686	Printed formulas (1% sample)	unsloth/LaTeX_OCR
v2 (this)	~145K	Printed + full coverage	+ linxy/LaTeX_OCR full dataset

What's New in v2?

• 2x More Data: ~145K vs ~68K samples
• Full Coverage: Uses complete linxy/LaTeX_OCR (not a subset)
• More Diversity: Broader range of formula types and complexities
• Better Generalization: Reduced overfitting risk with more unique examples

Dataset Structure

Data Fields

Field	Type	Description
messages	list[dict]	Multimodal conversation: user (image + instruction) + assistant (LaTeX)

Message Schema

messages[0] = {"role": "user", "content": [
    {"type": "text",  "text": "Write the LaTeX representation for this image."},
    {"type": "image", "image": <PIL.Image>}
]}
messages[1] = {"role": "assistant", "content": [
    {"type": "text", "text": "<latex_formula>"}
]}

Sources

Dataset	Config	Samples	Formula Types	Notes
unsloth/LaTeX_OCR	default	68,686	Printed	1% sample of linxy full
linxy/LaTeX_OCR	full	~76,318	Printed	Full printed text dataset

Source: LinXueyuanStdio/LaTeX_OCR — data from Zenodo, CROHME, and custom-built datasets. Validated with LaTeX AST parsing.

Format

{
  "messages": [
    {
      "role": "user",
      "content": [
        {
          "type": "text",
          "text": "Write the LaTeX representation for this image."
        },
        {
          "type": "image",
          "image": "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=320x64>"
        }
      ]
    },
    {
      "role": "assistant",
      "content": [
        {
          "type": "text",
          "text": "\\int_{0}^{\\infty} \\frac{x^{s-1}}{e^{x}-1} dx = \\Gamma(s) \\zeta(s)"
        }
      ]
    }
  ]
}

Sample Formula Examples

Image Description	LaTeX Output
Simple fraction	\frac{a}{b}
Summation	\sum_{i=1}^{n} x_i
Integral with limits	\int_{-\infty}^{\infty} e^{-x^2} dx = \sqrt{\pi}
Matrix	\begin{pmatrix} a & b \\ c & d \end{pmatrix}
Nested fraction	\frac{d}{dx}\left(\frac{f(x)}{g(x)}\right)

Usage

from datasets import load_dataset

dataset = load_dataset("Mustafaege/qwen3.5-vision-ocr-v2")

print(dataset)
# DatasetDict({
#     train: Dataset({features: ['messages'], num_rows: ~130000}),
#     test:  Dataset({features: ['messages'], num_rows: ~15000})
# })

# Access image and LaTeX
sample = dataset['train'][0]
image = sample['messages'][0]['content'][1]['image']  # PIL.Image
latex = sample['messages'][1]['content'][0]['text']   # LaTeX string
print(f"LaTeX: {latex}")

Training with Unsloth (VL)

from unsloth import FastVisionModel
from trl import SFTTrainer, SFTConfig
from unsloth import is_bfloat16_supported

model, tokenizer = FastVisionModel.from_pretrained(
    model_name = "unsloth/Qwen2-VL-7B-Instruct",
    max_seq_length = 2048,
    load_in_4bit = True,
)

model = FastVisionModel.get_peft_model(
    model,
    finetune_vision_layers = True,
    finetune_language_layers = True,
    r = 16, lora_alpha = 16,
)

trainer = SFTTrainer(
    model = model,
    tokenizer = tokenizer,
    train_dataset = dataset['train'],
    args = SFTConfig(
        per_device_train_batch_size = 2,
        gradient_accumulation_steps = 4,
        fp16 = not is_bfloat16_supported(),
        bf16 = is_bfloat16_supported(),
        max_seq_length = 2048,
        use_gradient_checkpointing = "unsloth",
    ),
)
trainer.train()

Related Datasets

Version	Samples	Link
v1	68,686	Mustafaege/qwen3.5-vision-ocr-v1
v2 (this)	~145K	Mustafaege/qwen3.5-vision-ocr-v2

License

Apache 2.0 — see LICENSE for details.

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Built for Qwen3.5-VL fine-tuning. Part of the Mustafaege model series.