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.
Training Data
Fine-tuned on 5,421 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 to 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
Training Details
- Base model: microsoft/Phi-3.5-mini-instruct
- Method: LoRA (r=16, alpha=16)
- Epochs: 3
- Training examples: 5,421
- Boards: AQA, Edexcel, OCR
- Levels: GCSE (Foundation and Higher), AS-Level, A-Level
- Subject: Mathematics
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)
- May not generalise to other curricula or subjects
How to Use
from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModel base_model = "microsoft/Phi-3.5-mini-instruct"model = AutoModelForCausalLM.from_pretrained(base_model)model = PeftModel.from_pretrained( model, "Osam-2025/quantosimo-maths-marking-v1")tokenizer = AutoTokenizer.from_pretrained(base_model)
Citation
@misc{osagiede2026quantosimomarking, author = {Osagiede, Samuel}, title = {QuantOsimo Maths Marking Model v1}, year = {2026}, url = {https://huggingface.co/Osam-2025/quantosimo-maths-marking-v1}}