---
tags:
- math-ocr
- latex
- unimer
- pytorch-lightning
---

# Texo — UniMER replication (step 1750 checkpoint)

Full fine-tune of [PP-FormulaNet-S](https://huggingface.co/alephpi/FormulaNet) on [m4xi/unimer-merged](https://huggingface.co/datasets/m4xi/unimer-merged) for math formula recognition (image → LaTeX).

## Base model

PP-FormulaNet-S (`formulanet.pt` from alephpi/FormulaNet): 58M parameter model with HGNetV2 encoder + MBart decoder. This is the distilled checkpoint from the original Texo paper, prior to any UniMER training.

## Training

| | |
|---|---|
| Dataset | m4xi/unimer-merged (~1.04M train samples, 98/2 train/val split) |
| Fine-tune strategy | Full (no LoRA) |
| Effective batch size | 64 (16 per device × 4 grad accum) |
| Learning rate | 1e-4, cosine decay |
| Precision | bf16 |
| Steps | 1,750 (~0.11 epochs, ~112K samples seen) |
| Hardware | NVIDIA RTX 4090, ~42h total runtime (crashed) |

## Checkpoint selection

The run crashed after ~70k steps. Validation BLEU peaked at step 1750 (BLEU=0.7247, edit_distance=0.0582) and degraded monotonically after that. `save_top_k=5` retained only the five best-BLEU checkpoints, all of which fell within the first ~15% of epoch 1; consistent with rapid convergence from a strong pretrained base.

## Usage

```python
import torch
from task import FormulaNetLit  # Texo src/task.py

model = FormulaNetLit.load_from_checkpoint("texo-cp-1750.ckpt", map_location="cpu")
model.eval()

outputs = model.generate(pixel_values, num_beams=1, do_sample=False, max_new_tokens=512)
pred = model.tokenizer.batch_decode(outputs, skip_special_tokens=True)
```