this repo has no description
Initial commit: Gemma 4 E2B QLoRA fine-tuning for Typst math OCR
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
+3377
+46
grammar.gbnf
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grammar.gbnf
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# Typst math grammar for llama-cli --grammar-file
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#
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# Conservative: enforces balanced brackets/parens/braces and rejects bare
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# unmatched delimiters. Does not fully parse Typst math -- the goal is to
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# eliminate the bracket-mismatch error class with zero training cost, not
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# to validate full Typst semantics.
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#
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# Usage:
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# ./llama-cli ... --grammar-file grammar.gbnf
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#
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# If the model struggles to complete under grammar constraints, widen the
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# grammar by replacing inner rules with `atom` permitting arbitrary chars.
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root ::= ws? expr ws?
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expr ::= item (ws? item)*
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item ::= paren-group
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| bracket-group
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| brace-group
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| script
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| atom
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# Balanced delimiter groups
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paren-group ::= "(" ws? expr? ws? ")"
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bracket-group ::= "[" ws? expr? ws? "]"
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brace-group ::= "{" ws? expr? ws? "}"
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# Super/subscript -- bound is a single atom or parenthesised expr
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script ::= "_" bound ("^" bound)?
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| "^" bound ("_" bound)?
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bound ::= paren-group | bracket-group | plain-atom
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# Atomic non-grouping token: identifier, number, operator, punctuation.
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# Does NOT include raw (, ), [, ], {, } -- those must be in a group.
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atom ::= plain-atom | "/" | "!" | "'"
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plain-atom ::= ident | number | operator
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ident ::= [a-zA-Z] [a-zA-Z0-9]*
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number ::= [0-9]+ ("." [0-9]+)?
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operator ::= "+" | "-" | "*" | "=" | "<" | ">" | "<=" | ">=" | "!="
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| "->" | "<-" | "," | ";" | ":" | "|" | "&" | "~" | "."
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ws ::= [ \t\n]+
+28
pyproject.toml
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pyproject.toml
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[project]
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name = "gemma-4-typst-ocr"
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version = "0.1.0"
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requires-python = ">=3.11"
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dependencies = [
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"unsloth[colab-new]",
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"trl>=0.15",
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"datasets>=2.19",
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"Pillow>=10",
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"torch>=2.3",
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"editdistance",
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"tqdm",
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]
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[project.scripts]
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train = "src.train:main"
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evaluate = "src.eval:main"
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mine = "src.mine_failures:main"
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train-hnm = "src.train_hnm:main"
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export = "src.export:main"
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generate-typeset = "src.generate_typeset:main"
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[build-system]
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requires = ["hatchling"]
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build-backend = "hatchling.build"
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[tool.hatch.build.targets.wheel]
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packages = ["src"]
src/__init__.py
src/__init__.py
This is a binary file and will not be displayed.
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src/data.py
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src/data.py
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"""
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Data loading for Gemma 4 vision fine-tuning.
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Manifests live in ../eff-mer/data/raster/<split>/manifest.jsonl.
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Each line: {"image": "images/xxx.png", "latex": "...", "typst": "..."}
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Images are loaded lazily via Dataset.set_transform -- never all in memory.
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Augmentation is applied on-the-fly during training only.
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"""
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import json
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import re
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import random
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from collections import defaultdict
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from pathlib import Path
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from torch.utils.data import Dataset
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from PIL import Image, ImageFilter
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import torchvision.transforms.functional as TF
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RASTER_ROOT = Path(__file__).parent.parent / "../eff-mer/data/raster"
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TYPESET_ROOT = Path(__file__).parent.parent / "data"
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TRAIN_SPLITS = [
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"crohme_gen_LaTeX_data_CROHME_2019",
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"crohme_gen_LaTeX_data_CROHME_2023_corpus",
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"crohme_gen_syntactic_data",
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"crohme_real_train",
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"mathwriting_Train",
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"mathwriting_Synthetic",
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"mathwriting_Symbols",
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]
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VAL_SPLITS = ["mathwriting_Val"]
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TEST_SPLITS = ["mathwriting_Test"]
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TYPESET_SPLITS = ["typeset"] # uv run generate-typeset
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TYPESET_VAL_SPLITS = ["typeset_val"] # uv run generate-typeset --out data/typeset_val --count 1000 --seed 43
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TYPESET_TEST_SPLITS = ["typeset_test"] # uv run generate-typeset --out data/typeset_test --count 500 --seed 44
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PROMPT = "Transcribe this mathematical expression to Typst math notation."
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BASE_MODEL = "unsloth/gemma-4-E2B-it"
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_NUM_RE = re.compile(r"\d+(\.\d+)?")
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_VAR_RE = re.compile(r"\b[a-zA-Z]\b")
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_MAX_PER_KEY = 5
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def _structural_key(typst: str) -> str:
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"""Normalize numbers and single-letter variables to expose structural pattern.
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x^2, y^3, a^9 all map to v^# -- same template, different surface tokens.
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Fractions like 1/2 vs 123/456 also collapse to #/#.
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"""
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s = _NUM_RE.sub("#", typst)
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s = _VAR_RE.sub("v", s)
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return s
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def load_records(split_names: list[str], dedupe: bool = True,
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root: Path = RASTER_ROOT) -> list[dict]:
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"""
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Load records from manifests.
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With dedupe=True applies two passes:
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1. Exact typst string dedup -- removes identical outputs across splits.
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2. Structural key cap (MAX_PER_KEY=5) -- allows limited numeric/variable
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variation per template while capping enumeration spam.
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"""
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seen_exact: set[str] = set()
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struct_counts: defaultdict[str, int] = defaultdict(int)
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records: list[dict] = []
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for name in split_names:
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manifest = root / name / "manifest.jsonl"
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base = (root / name).resolve()
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for line in manifest.read_text().splitlines():
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r = json.loads(line)
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typst = r.get("typst", "")
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if not typst or typst.startswith("ERROR:"):
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continue
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if dedupe:
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if typst in seen_exact:
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continue
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sk = _structural_key(typst)
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if struct_counts[sk] >= _MAX_PER_KEY:
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continue
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seen_exact.add(typst)
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struct_counts[sk] += 1
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records.append({
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"image_path": str(base / r["image"]),
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"typst": typst,
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})
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return records
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def _augment(img: Image.Image) -> Image.Image:
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"""
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Mild augmentation for synthetic-to-phone-photo robustness.
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Mirrors eff-mer's augmentation plus brightness/contrast jitter.
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"""
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angle = random.uniform(-5, 5)
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scale = random.uniform(0.9, 1.1)
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tx = int(random.uniform(-0.05, 0.05) * img.width)
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ty = int(random.uniform(-0.05, 0.05) * img.height)
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img = TF.affine(img, angle=angle, translate=(tx, ty), scale=scale, shear=0, fill=255)
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if random.random() < 0.6:
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img = img.filter(ImageFilter.GaussianBlur(random.uniform(0.0, 0.8)))
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img = TF.adjust_brightness(img, random.uniform(0.75, 1.25))
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img = TF.adjust_contrast(img, random.uniform(0.75, 1.25))
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return img
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class MathOCRDataset(Dataset):
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"""
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Torch Dataset with lazy image loading. Avoids HuggingFace datasets
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fingerprinting, which uses dill and breaks on Python 3.14.
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"""
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def __init__(self, records: list[dict], do_augment: bool = False) -> None:
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self.records = records
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self.do_augment = do_augment
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def __len__(self) -> int:
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return len(self.records)
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def __getitem__(self, idx: int) -> dict:
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r = self.records[idx]
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img = Image.open(r["image_path"]).convert("RGB")
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if self.do_augment:
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img = _augment(img)
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return {
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"messages": [
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{
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"role": "user",
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"content": [
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{"type": "image", "image": img},
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{"type": "text", "text": PROMPT},
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],
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},
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{
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"role": "assistant",
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"content": [{"type": "text", "text": r["typst"]}],
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},
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]
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}
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def make_dataset(records: list[dict], do_augment: bool = False) -> MathOCRDataset:
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return MathOCRDataset(records, do_augment)
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src/eval.py
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src/eval.py
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"""
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ExpRate evaluation on mathwriting_Test.
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ExpRate = fraction of exact string matches after whitespace normalization,
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matching the eff-mer definition (tokenizer encode→decode round-trip omitted
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here since we're comparing raw model output strings directly).
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Usage: uv run evaluate [--checkpoint checkpoints/baseline/final]
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"""
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import argparse
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import re
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import torch
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from tqdm import tqdm
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from unsloth import FastVisionModel
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from .data import BASE_MODEL, TEST_SPLITS, PROMPT, load_records
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def normalize(s: str) -> str:
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return re.sub(r"\s+", " ", s).strip()
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def extract_assistant(decoded: str) -> str:
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"""Pull the assistant turn out of a full decoded sequence."""
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# Gemma chat format ends assistant turn after <end_of_turn>
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if "<start_of_turn>model" in decoded:
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decoded = decoded.split("<start_of_turn>model")[-1]
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if "<end_of_turn>" in decoded:
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decoded = decoded.split("<end_of_turn>")[0]
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return decoded.strip()
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def evaluate(checkpoint: str, batch_size: int = 8) -> float:
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model, processor = FastVisionModel.from_pretrained(checkpoint, load_in_4bit=True)
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FastVisionModel.for_inference(model)
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model.eval()
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records = load_records(TEST_SPLITS, dedupe=False)
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correct = 0
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from PIL import Image
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for i in tqdm(range(0, len(records), batch_size), desc="Evaluating"):
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batch = records[i : i + batch_size]
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for r in batch:
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img = Image.open(r["image_path"]).convert("RGB")
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messages = [
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{
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"role": "user",
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"content": [
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{"type": "image", "image": img},
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{"type": "text", "text": PROMPT},
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],
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}
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]
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inputs = processor.apply_chat_template(
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messages,
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add_generation_prompt=True,
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return_tensors="pt",
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).to("cuda")
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with torch.no_grad():
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out = model.generate(**inputs, max_new_tokens=256, do_sample=False)
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decoded = processor.decode(out[0], skip_special_tokens=False)
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pred = extract_assistant(decoded)
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if normalize(pred) == normalize(r["typst"]):
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correct += 1
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exprate = correct / len(records)
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print(f"ExpRate: {exprate:.4f} ({correct}/{len(records)})")
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return exprate
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def main() -> None:
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parser = argparse.ArgumentParser()
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parser.add_argument("--checkpoint", default="checkpoints/baseline/final")
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parser.add_argument("--batch-size", type=int, default=8)
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args = parser.parse_args()
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evaluate(args.checkpoint, args.batch_size)
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if __name__ == "__main__":
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main()
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src/export.py
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src/export.py
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"""
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Export fine-tuned model to GGUF Q8_0 for llama-cli inference.
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Output: gemma4-math-ocr-Q8_0.gguf (in current directory)
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Usage: uv run export [--checkpoint checkpoints/hnm/final]
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[--quant q8_0]
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"""
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import argparse
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from unsloth import FastVisionModel
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def main() -> None:
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parser = argparse.ArgumentParser()
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parser.add_argument("--checkpoint", default="checkpoints/hnm/final")
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parser.add_argument("--quant", default="q8_0")
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parser.add_argument("--out", default="gemma4-math-ocr")
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args = parser.parse_args()
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model, processor = FastVisionModel.from_pretrained(args.checkpoint, load_in_4bit=True)
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model.save_pretrained_gguf(args.out, processor, quantization_method=args.quant)
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print(f"Exported to {args.out}-{args.quant.upper()}.gguf")
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if __name__ == "__main__":
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main()
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src/generate_typeset.py
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src/generate_typeset.py
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"""
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Generate typeset math images from a recursive grammar.
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Renders diverse Typst math expressions to PNG via `typst compile` and writes
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a manifest.jsonl compatible with load_records().
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After running, add "typeset" to TYPESET_SPLITS in data.py and mix into training.
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Usage: uv run generate-typeset [--count 8000] [--out data/typeset]
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[--jobs 4] [--seed 42]
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"""
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import argparse
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import hashlib
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import json
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import random
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import subprocess
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import tempfile
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from concurrent.futures import ThreadPoolExecutor, as_completed
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from pathlib import Path
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from tqdm import tqdm
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from .data import _structural_key
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# ── Grammar ───────────────────────────────────────────────────────────────────
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_VARS = ["x", "y", "z", "n", "a", "b", "t", "k", "m", "i", "j", "r", "s", "u", "v"]
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_GREEK = ["alpha", "beta", "theta", "lambda", "mu", "sigma", "pi", "phi",
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"omega", "epsilon", "delta", "gamma", "rho", "nu", "eta", "xi", "zeta"]
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_NUMS = ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "100", "p", "q"]
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_FUNCS = ["sin", "cos", "tan", "ln", "log", "exp", "arcsin", "arccos",
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"arctan", "sinh", "cosh", "tanh"]
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_RELS = ["=", "!=", "<=", ">=", "<", ">", "approx", "equiv"]
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_OPS = ["+", "-", "dot", "times"]
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_TO = ["0", "oo", "1", "-oo", "infinity"]
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+
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def _atom(rng: random.Random) -> str:
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r = rng.random()
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if r < 0.45:
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return rng.choice(_VARS)
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elif r < 0.65:
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return rng.choice(_GREEK)
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else:
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return rng.choice(_NUMS)
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+
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+
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def _expr(rng: random.Random, depth: int = 0, max_depth: int = 3) -> str:
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if depth >= max_depth or (depth > 0 and rng.random() < 0.35):
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return _atom(rng)
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+
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c = rng.random()
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+
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if c < 0.10:
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return f"frac({_expr(rng, depth+1)}, {_expr(rng, depth+1)})"
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elif c < 0.17:
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return f"{_atom(rng)}^({_expr(rng, depth+1)})"
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elif c < 0.23:
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return f"{_atom(rng)}^{_atom(rng)}"
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elif c < 0.28:
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a, s, p = _atom(rng), _atom(rng), _atom(rng)
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return f"{a}_{s}^{p}"
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elif c < 0.34:
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v, lo, hi = _atom(rng), _atom(rng), _atom(rng)
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return f"sum_({v} = {lo})^{hi} {_expr(rng, depth+1)}"
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elif c < 0.39:
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lo, hi = _atom(rng), _atom(rng)
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dv = rng.choice(["x", "y", "t"])
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return f"integral_{lo}^{hi} {_expr(rng, depth+1)} dif {dv}"
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+
elif c < 0.44:
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return f"{rng.choice(_FUNCS)}({_expr(rng, depth+1)})"
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elif c < 0.48:
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return f"sqrt({_expr(rng, depth+1)})"
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elif c < 0.52:
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n = rng.randint(2, 5)
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return f"root({n}, {_expr(rng, depth+1)})"
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elif c < 0.56:
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v = rng.choice(_VARS)
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to = rng.choice(_TO)
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return f"lim_({v} -> {to}) {_expr(rng, depth+1)}"
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+
elif c < 0.60:
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return f"binom({_atom(rng)}, {_atom(rng)})"
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+
elif c < 0.67:
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op = rng.choice(_OPS)
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return f"({_expr(rng, depth+1)} {op} {_expr(rng, depth+1)})"
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elif c < 0.72:
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rel = rng.choice(_RELS)
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return f"{_expr(rng, depth+1)} {rel} {_expr(rng, depth+1)}"
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+
elif c < 0.76:
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+
v, lo, hi = _atom(rng), _atom(rng), _atom(rng)
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return f"product_({v} = {lo})^{hi} {_expr(rng, depth+1)}"
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+
elif c < 0.80:
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+
e = [_atom(rng) for _ in range(4)]
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return f"mat({e[0]}, {e[1]}; {e[2]}, {e[3]})"
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elif c < 0.84:
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f_var = rng.choice(_VARS)
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x_var = rng.choice(_VARS)
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return f"(dif {f_var}) / (dif {x_var})"
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elif c < 0.88:
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f_var = rng.choice(_VARS)
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x_var = rng.choice(_VARS)
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return f"(diff {f_var}) / (diff {x_var})"
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elif c < 0.92:
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return f"norm({_expr(rng, depth+1)})"
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+
else:
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return _atom(rng)
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+
110
+
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def generate_expr(rng: random.Random) -> str:
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return _expr(rng, depth=0, max_depth=3)
113
+
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+
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# ── Rendering ─────────────────────────────────────────────────────────────────
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+
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_TEMPLATE = (
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"#set page(width: auto, height: auto, "
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"margin: (x: 10pt, y: 8pt), fill: white)\n"
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"$ {math} $\n"
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)
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+
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+
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+
def render_one(math: str, out_path: Path) -> bool:
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+
"""Compile one Typst math string to PNG. Returns True on success."""
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+
src = _TEMPLATE.format(math=math)
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+
with tempfile.NamedTemporaryFile(suffix=".typ", mode="w", delete=False) as f:
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f.write(src)
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typ_path = Path(f.name)
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+
try:
131
+
result = subprocess.run(
132
+
["typst", "compile", "--format", "png", "--ppi", "150",
133
+
str(typ_path), str(out_path)],
134
+
capture_output=True,
135
+
timeout=15,
136
+
)
137
+
return result.returncode == 0
138
+
except (subprocess.TimeoutExpired, FileNotFoundError):
139
+
return False
140
+
finally:
141
+
typ_path.unlink(missing_ok=True)
142
+
143
+
144
+
# ── Main ──────────────────────────────────────────────────────────────────────
145
+
146
+
def main() -> None:
147
+
parser = argparse.ArgumentParser()
148
+
parser.add_argument("--count", type=int, default=8_000,
149
+
help="Target unique structural templates to generate")
150
+
parser.add_argument("--out", default="data/typeset")
151
+
parser.add_argument("--jobs", type=int, default=4)
152
+
parser.add_argument("--seed", type=int, default=42)
153
+
args = parser.parse_args()
154
+
155
+
out = Path(args.out)
156
+
img_dir = out / "images"
157
+
img_dir.mkdir(parents=True, exist_ok=True)
158
+
159
+
rng = random.Random(args.seed)
160
+
161
+
# Phase 1: sample until we hit --count unique structural keys
162
+
print(f"Generating {args.count:,} structurally unique expressions ...")
163
+
seen_struct: set[str] = set()
164
+
candidates: list[str] = []
165
+
attempts = 0
166
+
167
+
with tqdm(total=args.count, unit="expr") as pbar:
168
+
while len(candidates) < args.count:
169
+
attempts += 1
170
+
math = generate_expr(rng)
171
+
sk = _structural_key(math)
172
+
if sk in seen_struct:
173
+
continue
174
+
seen_struct.add(sk)
175
+
candidates.append(math)
176
+
pbar.update(1)
177
+
178
+
print(f" {attempts:,} attempts, {attempts / len(candidates):.1f}x overhead")
179
+
180
+
# Phase 2: render in parallel
181
+
print(f"Rendering {len(candidates):,} images with {args.jobs} workers ...")
182
+
records: list[dict] = []
183
+
failures: int = 0
184
+
185
+
def _task(math: str) -> tuple[str, str, bool]:
186
+
h = hashlib.sha1(math.encode()).hexdigest()[:16]
187
+
out_path = img_dir / f"{h}.png"
188
+
ok = render_one(math, out_path)
189
+
return math, f"images/{h}.png", ok
190
+
191
+
with ThreadPoolExecutor(max_workers=args.jobs) as pool:
192
+
futs = {pool.submit(_task, m): m for m in candidates}
193
+
with tqdm(total=len(candidates), unit="img") as pbar:
194
+
for fut in as_completed(futs):
195
+
math, rel_path, ok = fut.result()
196
+
if ok:
197
+
records.append({"image": rel_path, "typst": math})
198
+
else:
199
+
failures += 1
200
+
pbar.update(1)
201
+
202
+
# Phase 3: manifest
203
+
manifest = out / "manifest.jsonl"
204
+
with manifest.open("w") as f:
205
+
for r in records:
206
+
f.write(json.dumps(r) + "\n")
207
+
208
+
print(f"Wrote {len(records):,} records to {manifest} ({failures} render failures)")
209
+
print("Add 'typeset' to TYPESET_SPLITS in data.py and mix into train_records.")
210
+
211
+
212
+
if __name__ == "__main__":
213
+
main()
+86
src/train.py
+86
src/train.py
···
1
+
"""
2
+
Baseline QLoRA fine-tuning of Gemma 4 E2B for Typst math OCR.
3
+
4
+
Usage: uv run train
5
+
"""
6
+
7
+
from unsloth import FastVisionModel
8
+
from unsloth.trainer import UnslothVisionDataCollator
9
+
from trl import SFTTrainer, SFTConfig
10
+
11
+
from .data import (BASE_MODEL, TRAIN_SPLITS, VAL_SPLITS, TYPESET_SPLITS,
12
+
TYPESET_VAL_SPLITS, TYPESET_ROOT, load_records, make_dataset)
13
+
14
+
15
+
def main() -> None:
16
+
model, processor = FastVisionModel.from_pretrained(
17
+
BASE_MODEL,
18
+
load_in_4bit=True,
19
+
use_gradient_checkpointing="unsloth",
20
+
)
21
+
22
+
model = FastVisionModel.get_peft_model(
23
+
model,
24
+
finetune_vision_layers=True, # critical: vision encoder must adapt
25
+
finetune_language_layers=True,
26
+
finetune_attention_modules=True,
27
+
finetune_mlp_modules=True,
28
+
r=32,
29
+
lora_alpha=32,
30
+
lora_dropout=0,
31
+
bias="none",
32
+
)
33
+
34
+
train_records = load_records(TRAIN_SPLITS, dedupe=True)
35
+
typeset_manifest = TYPESET_ROOT / "typeset" / "manifest.jsonl"
36
+
if typeset_manifest.exists():
37
+
typeset_records = load_records(TYPESET_SPLITS, dedupe=False, root=TYPESET_ROOT)
38
+
train_records += typeset_records
39
+
print(f"Typeset train: {len(typeset_records):,} records mixed in")
40
+
val_records = load_records(VAL_SPLITS, dedupe=False)
41
+
typeset_val_manifest = TYPESET_ROOT / "typeset_val" / "manifest.jsonl"
42
+
if typeset_val_manifest.exists():
43
+
typeset_val_records = load_records(TYPESET_VAL_SPLITS, dedupe=False, root=TYPESET_ROOT)
44
+
val_records += typeset_val_records
45
+
print(f"Typeset val: {len(typeset_val_records):,} records mixed in")
46
+
print(f"Train: {len(train_records):,} Val: {len(val_records):,}")
47
+
48
+
train_ds = make_dataset(train_records, do_augment=True)
49
+
val_ds = make_dataset(val_records, do_augment=False)
50
+
51
+
trainer = SFTTrainer(
52
+
model=model,
53
+
tokenizer=processor,
54
+
data_collator=UnslothVisionDataCollator(model, processor),
55
+
train_dataset=train_ds,
56
+
eval_dataset=val_ds,
57
+
args=SFTConfig(
58
+
per_device_train_batch_size=4,
59
+
per_device_eval_batch_size=4,
60
+
gradient_accumulation_steps=4, # 4 * 4 = 16, same effective batch
61
+
num_train_epochs=3,
62
+
learning_rate=2e-4,
63
+
warmup_steps=500,
64
+
lr_scheduler_type="cosine",
65
+
bf16=True,
66
+
fp16=False,
67
+
dataloader_num_workers=0, # required for set_transform + PIL
68
+
logging_steps=50,
69
+
eval_strategy="steps",
70
+
eval_steps=500,
71
+
save_steps=100, # ~14 min to first checkpoint; eval every 500
72
+
save_total_limit=10,
73
+
load_best_model_at_end=False,
74
+
output_dir="checkpoints/baseline",
75
+
dataset_kwargs={"skip_prepare_dataset": True},
76
+
),
77
+
)
78
+
79
+
trainer.train()
80
+
model.save_pretrained("checkpoints/baseline/final")
81
+
processor.save_pretrained("checkpoints/baseline/final")
82
+
print("Saved to checkpoints/baseline/final")
83
+
84
+
85
+
if __name__ == "__main__":
86
+
main()
+2738
uv.lock
+2738
uv.lock
···
1
+
version = 1
2
+
revision = 3
3
+
requires-python = ">=3.11"
4
+
resolution-markers = [
5
+
"python_full_version >= '3.14' and sys_platform == 'win32'",
6
+
"python_full_version >= '3.14' and sys_platform == 'emscripten'",
7
+
"python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
8
+
"python_full_version >= '3.12' and python_full_version < '3.14' and sys_platform == 'win32'",
9
+
"python_full_version >= '3.12' and python_full_version < '3.14' and sys_platform == 'emscripten'",
10
+
"python_full_version >= '3.12' and python_full_version < '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
11
+
"python_full_version < '3.12' and sys_platform == 'win32'",
12
+
"python_full_version < '3.12' and sys_platform == 'emscripten'",
13
+
"python_full_version < '3.12' and sys_platform != 'emscripten' and sys_platform != 'win32'",
14
+
]
15
+
16
+
[[package]]
17
+
name = "accelerate"
18
+
version = "1.13.0"
19
+
source = { registry = "https://pypi.org/simple" }
20
+
dependencies = [
21
+
{ name = "huggingface-hub" },
22
+
{ name = "numpy" },
23
+
{ name = "packaging" },
24
+
{ name = "psutil" },
25
+
{ name = "pyyaml" },
26
+
{ name = "safetensors" },
27
+
{ name = "torch" },
28
+
]
29
+
sdist = { url = "https://files.pythonhosted.org/packages/ca/14/787e5498cd062640f0f3d92ef4ae4063174f76f9afd29d13fc52a319daae/accelerate-1.13.0.tar.gz", hash = "sha256:d631b4e0f5b3de4aff2d7e9e6857d164810dfc3237d54d017f075122d057b236", size = 402835, upload-time = "2026-03-04T19:34:12.359Z" }
30
+
wheels = [
31
+
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32
+
]
33
+
34
+
[[package]]
35
+
name = "aiohappyeyeballs"
36
+
version = "2.6.1"
37
+
source = { registry = "https://pypi.org/simple" }
38
+
sdist = { url = "https://files.pythonhosted.org/packages/26/30/f84a107a9c4331c14b2b586036f40965c128aa4fee4dda5d3d51cb14ad54/aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558", size = 22760, upload-time = "2025-03-12T01:42:48.764Z" }
39
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wheels = [
40
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41
+
]
42
+
43
+
[[package]]
44
+
name = "aiohttp"
45
+
version = "3.13.5"
46
+
source = { registry = "https://pypi.org/simple" }
47
+
dependencies = [
48
+
{ name = "aiohappyeyeballs" },
49
+
{ name = "aiosignal" },
50
+
{ name = "attrs" },
51
+
{ name = "frozenlist" },
52
+
{ name = "multidict" },
53
+
{ name = "propcache" },
54
+
{ name = "yarl" },
55
+
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56
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sdist = { url = "https://files.pythonhosted.org/packages/77/9a/152096d4808df8e4268befa55fba462f440f14beab85e8ad9bf990516918/aiohttp-3.13.5.tar.gz", hash = "sha256:9d98cc980ecc96be6eb4c1994ce35d28d8b1f5e5208a23b421187d1209dbb7d1", size = 7858271, upload-time = "2026-03-31T22:01:03.343Z" }
57
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wheels = [
58
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60
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2004
+
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2005
+
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2006
+
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2007
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2008
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2009
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2010
+
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2011
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2012
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2013
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2014
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2015
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2016
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2017
+
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2018
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2019
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2020
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2021
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2022
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2023
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2024
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2025
+
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2026
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2027
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2028
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2029
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2030
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2031
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2032
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2033
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2034
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2035
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2036
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2037
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2038
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2039
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2040
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2041
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2042
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2043
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2044
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2045
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2046
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2047
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2048
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2049
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