README.md

---
language:
- mr
- en
base_model: microsoft/Phi-3-mini-4k-instruct
tags:
- marathi
- indian-language
- lora
- peft
- fine-tuned
- education
- kids
license: mit
---

# 🌸 Marathi Mitra — माझा मराठी मित्र

Fine-tuned Phi-3 Mini for Marathi vocabulary learning,
built as a personalized tool to help my daughter learn Marathi.

## Model Details

| Property | Value |
|----------|-------|
| Base Model | microsoft/Phi-3-mini-4k-instruct |
| Fine-tuning Method | QLoRA (SFT) |
| LoRA Rank | r=32, alpha=64 |
| Training Examples | 30 Marathi vocabulary items |
| Best Experiment | exp4_lr2e4_epochs25_r32 |
| Format Score | 36.4% |
| Training Hardware | Google Colab T4 GPU |

## What It Does

Given an English word, generates a Marathi lesson with:
- Marathi word in Devanagari script
- Pronunciation guide
- Example sentence
- Fun fact for kids

## How to Use

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

base = AutoModelForCausalLM.from_pretrained(
    "microsoft/Phi-3-mini-4k-instruct",
    torch_dtype=torch.float16,
    trust_remote_code=True,
)
model = PeftModel.from_pretrained(base, "ninadp/marathi-mitra-phi3")
tokenizer = AutoTokenizer.from_pretrained(
    "microsoft/Phi-3-mini-4k-instruct",
    trust_remote_code=True,
)

prompt = """### Instruction:
You are Marathi Mitra, a friendly Marathi teacher for kids.

### Input:
Teach me the Marathi word for: butterfly

### Response:
"""
inputs = tokenizer(prompt, return_tensors="pt")
output = model.generate(**inputs, max_new_tokens=150)
print(tokenizer.decode(output[0], skip_special_tokens=True))
```

## Training Details

Fine-tuned using Supervised Fine-Tuning (SFT) with QLoRA
on 30 Marathi vocabulary examples across 4 hyperparameter
experiments.

| Experiment | LR | Epochs | Loss | Score |
|------------|-----|--------|------|-------|
| Baseline | N/A | N/A | N/A | 11.2% |
| Exp1 | 2e-4 | 5 | 1.29 | 12.8% |
| Exp2 | 2e-4 | 25 | 0.20 | 28.8% |
| Exp3 | 1e-4 | 25 | 0.37 | 16.0% |
| Exp4 | 2e-4 | 25 | 0.22 | 36.4% ✅ |

## Limitations

- Trained on only 30 examples — vocabulary coverage is limited
- May generate incorrect Marathi words for unseen vocabulary
- Format learned well; accuracy improves with more data
- Retraining with 200+ examples planned

## Live Demo

[🚀 Try it on HF Spaces](https://huggingface.co/spaces/ninadp/marathi-mitra)

## GitHub

[📦 Full project code](https://github.com/ninadparab/marathi-mitra)