It is not a solver and not a teaching model.
It is a conservative pre-processor that rewrites noisy OCR text into a JSON object that downstream services can safely consume.

Task

Input: raw OCR text from a math exam question

Output:

json

{
  "stem": "cleaned problem statement",
  "answer_raw": "raw answer if clearly visible, otherwise empty",
  "solution_raw": "",
  "ocr_notes": ["risk tag 1", "risk tag 2"]
}

Intended boundary

This adapter is designed to sit on a separate line:

OCR -> OCR rebuilder -> existing GPT teaching chain

It should:

improve stem
improve answer_raw
reduce hallucinated answers
add conservative OCR risk notes

It should not:

replace your main GPT explanation model
solve the math problem
generate a polished solution_raw

At the current stage, solution_raw is intentionally kept empty.

Why this adapter exists

The base model can often emit valid JSON, but it tends to:

hallucinate answers when gold should be empty
drift away from the intended field semantics
over-talk beyond the strict OCR rebuild task

This adapter is optimized for a more conservative behavior.

Main test comparison

Evaluation setting:

base model: Qwen/Qwen2.5-3B-Instruct
adapter: current best stage-1 protocol-only LoRA
prompt: conservative non-solver prompt
generation: max_new_tokens=192
test set: 30 held-out samples

Metric table

Metric	Base model	Stage-1 adapter
JSON parse rate	80.00%	76.67%
`stem` exact match	0.00%	16.67%
`answer_raw` exact match	16.67%	60.00%
empty-answer hallucination	23.33%	0.00%

Visual comparison

JSON parse rate

text

Base model      80.00%  ████████████████
Stage-1 adapter 76.67%  ███████████████

answer_raw exact match

text

Base model      16.67%  ███
Stage-1 adapter 60.00%  ████████████

empty-answer hallucination (lower is better)

text

Base model      23.33%  █████
Stage-1 adapter  0.00%

Semantic fidelity

We also measured average character-level similarity against gold labels on the same held-out test set.

Field	Base model	Stage-1 adapter
`stem` avg similarity	0.4898	0.7217
`answer_raw` avg similarity	0.4058	0.6667
`ocr_notes` avg similarity	0.1597	0.2391

Visual comparison

stem average similarity

text

Base model      0.4898  ██████████
Stage-1 adapter 0.7217  ██████████████

answer_raw average similarity

text

Base model      0.4058  ████████
Stage-1 adapter 0.6667  █████████████

What this means

The adapter gives up a small amount of parse rate, but buys back the behaviors that matter most for this task:

much better answer_raw
much better stem
zero hallucinated answers on gold-empty cases

For an OCR rebuilding module that feeds a larger teaching system, this tradeoff is usually worth it.

Dataset summary

The project used two task buckets during development:

single_problem_rebuild: 204 synthetic/curated samples
multi_problem_fragment_rebuild: 102 synthetic/curated samples

The released adapter comes from a stage-1 protocol-only training setup that focused on:

one JSON object only
fixed field schema
conservative extraction
no solution_raw generation

Stage-1 smoke subset:

train: 32
dev: 8

Known limitations

solution_raw is intentionally weak and currently fixed to empty.
ocr_notes is helpful but not yet fully normalized.
Multi-problem mixed fragments are harder than single-problem OCR cleanup.
This is a task adapter, not a general OCR foundation model.

Deployment

This repository includes a handler.py for Hugging Face Inference Endpoints custom deployment.

Recommended input:

json

{
  "inputs": "raw OCR text"
}

Recommended output:

json

{
  "stem": "...",
  "answer_raw": "...",
  "solution_raw": "",
  "ocr_notes": ["..."],
  "meta": {
    "raw_ocr_notes": ["model raw notes"]
  }
}

Local usage

python

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
import torch

base_model = "Qwen/Qwen2.5-3B-Instruct"
adapter_model = "maru979/qwen2.5-3b-teacher-ocr-rebuilder"

tokenizer = AutoTokenizer.from_pretrained(base_model, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    base_model,
    torch_dtype=torch.float16,
    device_map="auto",
    trust_remote_code=True,
)
model = PeftModel.from_pretrained(model, adapter_model)
model.eval()

If you deploy this adapter as an endpoint, prefer the included handler.py instead of directly exposing raw generation.