Osam-2025

quantosimo-maths-marking-v1.1

README

License: apache-2.0

Model Description

A LoRA fine-tuned version of Phi-3.5-mini-instruct for marking GCSE, AS-Level and A-Level Mathematics answers against official mark schemes from AQA, Edexcel and OCR.

Built by QuantOsimo — an AI-powered Maths and Science tutoring platform for UK students KS2 to A-Level.

This is v1.1 — trained without 2023-2024 papers to allow genuine held-out evaluation. See v1 for the full 2018-2025 trained model.

Evaluation Results — Genuine Held-out

Evaluated on 50 questions from 2023-2024 papers not seen during training.

Overall Performance

Attempted correctly: 43/50 (86%)
Wrong or unreadable input: 7/50 (14%)

By Exam Board

Table with columns: Board, Score
Board	Score
AQA	82% (14/17)
Edexcel	100% (17/17)
OCR	75% (12/16)

By Level

Table with columns: Level, Score
Level	Score
GCSE	88% (30/34)
AS-Level	100% (10/10)
A-Level	50% (3/6)

Notes

v1 in-distribution score: 88% vs v1.1 held-out: 86% Only 2% drop — model generalises well to unseen papers
5 of 7 wrong answers had garbled PDF input text (unreadable questions — not model errors)
OCR lower score reflects PDF extraction quality issues
A-Level based on only 6 questions — too small to be conclusive
Edexcel and AS-Level: perfect scores across all questions

Training Details

Base model: microsoft/Phi-3.5-mini-instruct
Method: LoRA (r=16, alpha=16)
Epochs: 3
Training examples: 4,066
Years: 2018-2022 and 2025 (2023-2024 held out for evaluation)
Boards: AQA, Edexcel, OCR
Levels: GCSE (Foundation and Higher), AS-Level, A-Level
Subject: Mathematics
Training time: 116 minutes on Kaggle T4 x2

Training Data

Fine-tuned on 4,066 examples extracted from official past examination papers and mark schemes published by AQA, Pearson Edexcel and OCR for GCSE, AS-Level and A-Level Mathematics, covering the years 2018-2022 and 2025.

Training data is not published due to exam board copyright. A sample of 100 examples is available at: uk-gcse-alevel-maths-markschemes-2018-2025

Intended Use

Automated marking of student answers in EdTech platforms
Research into automated assessment
Educational AI development

Limitations

Only trained on Mathematics
UK curriculum only (AQA, Edexcel, OCR)
Some OCR papers have garbled PDF extraction affecting results
A-Level evaluation based on small sample (6 questions)

How to Use

python

from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModelimport torch base_model = "microsoft/Phi-3.5-mini-instruct"model = AutoModelForCausalLM.from_pretrained(    base_model,    torch_dtype=torch.float16,    device_map="auto",    attn_implementation="eager")model = PeftModel.from_pretrained(    model,    "Osam-2025/quantosimo-maths-marking-v1.1")tokenizer = AutoTokenizer.from_pretrained(base_model)

Citation

bibtex

@misc{osagiede2026quantosimomarking,  author = {Osagiede, Samuel},  title  = {QuantOsimo Maths Marking Model v1.1},  year   = {2026},  url    = {https://huggingface.co/Osam-2025/quantosimo-maths-marking-v1.1}}

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Model Details

Model Provider

Osam-2025

Model Tree

Base

microsoft/Phi-3.5-mini-instruct

Adapter

this model

Input Modalities

Text

Output Modalities