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aamingem/
texformer-576m-fp32

Name: texformer-576m-fp32
Availability: InStock

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texformer-576m-fp32

This repository contains a TeXformer 576m checkpoint in fp32 precision for OCR-to-LaTeX generation.

This is a custom TeXformer architecture checkpoint (model.pt) plus tokenizer assets. It is not a standard transformers AutoModel checkpoint. This export is derived from a checkpoint trained in bf16.

Files

model.pt: TeXformer checkpoint
tokenizer/pdf_tokenizer.json: PDF-side tokenizer
tokenizer/latex_tokenizer.json: LaTeX-side tokenizer
tokenizer/pdf_tags.json: frequent PDF tag metadata
tokenizer/latex_commands.json: frequent LaTeX command metadata

Architecture

Parameters (deduplicated): 576,538,624
Parameters (state_dict entries): 625,690,624
Encoder layers: 16
Decoder layers: 16
Hidden size (d_model): 1024
Attention heads: 16
Feed-forward size (d_ff): 4224
Max encoder length: 2560
Max decoder length: 2560
Stored precision: fp32

Quantization

Quantization method: fp32
Checkpoint payload key: model_state_dict
Original model training precision: bf16
Sample tensor dtype: torch.float32

Usage

python

from pathlib import Path
import torch
from huggingface_hub import snapshot_download
from texformer.models.checkpoint_loader import load_texformer_model
from texformer.tokenization.tokenizer import TeXFormerTokenizer

repo_id = "aamingem/texformer-576m-fp32"
local_dir = Path(snapshot_download(repo_id=repo_id))
tokenizer_dir = local_dir / "tokenizer"

tokenizer = TeXFormerTokenizer(tokenizer_dir)
if torch.cuda.is_available():
    device = torch.device("cuda")
elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
    device = torch.device("mps")
else:
    device = torch.device("cpu")
model, global_step, epoch, missing, unexpected = load_texformer_model(
    checkpoint_path=local_dir / "model.pt",
    tokenizer=tokenizer,
    device=device,
)
print("Loaded model:", model.__class__.__name__)
print("Missing keys:", len(missing), "Unexpected keys:", len(unexpected))

Intended Use

OCR-to-LaTeX / PDF-text-to-LaTeX sequence generation
Research and experimentation on scientific document conversion

Limitations

May produce incorrect or non-compiling LaTeX.
Performance depends on input extraction quality.
Not intended for high-stakes use without human verification.