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AddisuSeteye/
AI_Math_Tutor_for_Early_Learners

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AI Math Tutor for Early Learners

Numeracy-tuned small language model for short, child-friendly math explanations.
This repository contains the merged float16 checkpoint (all weights in one folder: model.safetensors + config + tokenizer files).


Model page	https://huggingface.co/AddisuSeteye/AI_Math_Tutor_for_Early_Learners
Source code	https://github.com/AdaSeteye/AI_Math_Tutor_for_Early_Learners
License	MIT

Model details

Architecture: GPT2LMHeadModel (6 layers, 768 hidden size, 12 attention heads) — same family as distilgpt2 used as the base for the training pipeline in this project.
Format: Merged full model in FP16 (model.safetensors), with config.json, generation_config.json, and tokenizer files.
Training: Supervised fine-tuning with LoRA (QLoRA when 4-bit is available), then merge into a single Hugging Face–compatible directory (merged_f16).
Training data: numeracy_instruct.jsonl — short user/assistant chat-style turns (counting, addition, subtraction, simple word problems) for early numeracy.
Intended use: Prototype / teaching demo for an AI Math Tutor context; not a production safety-critical system for unsupervised use with children without human oversight.

Files in this repo (upload from `merged_f16`)

File	Role
`model.safetensors`	Merged model weights (FP16)
`config.json`	Model configuration
`generation_config.json`	Default generation settings
`tokenizer.json`	Tokenizer (JSON)
`tokenizer_config.json`	Tokenizer config

How to use

python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "AddisuSeteye/AI_Math_Tutor_for_Early_Learners"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
)

# Example: single-turn style prompt (match your training format in practice)
text = "You are a math helper. How many is 2 + 1?"
inputs = tokenizer(text, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=80, do_sample=True, top_p=0.9)
print(tokenizer.decode(out[0], skip_special_tokens=True))

Requirements

transformers (version compatible with your config.json, e.g. 4.36+)
torch
safetensors (for loading .safetensors)

Limitations

Trained for short numeracy-style responses; may hallucinate or be incorrect on out-of-distribution questions.
Not a replacement for a teacher or parent; early-learner products should add safety, privacy, and pedagogy review.
If you re-train on a different base (e.g. a larger chat model) and re-upload, update this card to match the new config.json.

Citation

If you use this model, please cite the project repository and the Hugging Face model page. Example:

bibtex

@misc{aimathtutor2026,
  title   = {AI Math Tutor for Early Learners},
  author  = {Addisu Seteye},
  year    = {2026},
  howpublished = {\url{https://huggingface.co/AddisuSeteye/AI_Math_Tutor_for_Early_Learners}}
}

Project context

This checkpoint is one deliverable of the S2.T3.1 numeracy tutor work: instruction data in data/T3.1_Math_Tutor/, training script tutor/llm_qlora.py (see the GitHub repo for the full app, curriculum, and on-device tutor pipeline). The Gradio child demo in that repo does not load this merged checkpoint by default; the main product loop uses a separate pipeline (TTS, ASR, curriculum) described in the GitHub README.md.

AddisuSeteye/AI_Math_Tutor_for_Early_Learners