---
language:
- ur
- en
license: apache-2.0
base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit
tags:
- urdu
- low-resource-language
- instruction-tuning
- lora
- phi-3
- nlp
- south-asian-languages
- unsloth
---

# Phi3-UrduInstruct

A fine-tuned version of Microsoft's Phi-3-mini-4k-instruct 
for Urdu language instruction following.

## Model Description

Phi3-UrduInstruct is fine-tuned on a custom Urdu instruction 
dataset of 578 manually curated and verified examples. The 
model is designed to follow instructions in Urdu across 
multiple NLP tasks.

This work addresses the lack of instruction-tuned language 
models for Urdu, a low-resource language spoken by over 
230 million people worldwide.

## Training Data

A custom dataset of 578 Urdu instruction-response pairs 
was created for this project, covering 6 task categories:

| Category | Examples |
|---|---|
| Translation (Urdu → English) | 105 |
| Grammar Correction | 100 |
| Question Answering | 100 |
| Text Summarization | 107 |
| Text Completion | 91 |
| Formal/Informal Conversion | 75 |
| **Total** | **578** |

All examples were manually written and verified by a 
native Urdu speaker to ensure linguistic quality and 
cultural accuracy.

## Training Details

| Parameter | Value |
|---|---|
| Base Model | Phi-3-mini-4k-instruct (4-bit) |
| Fine-tuning Method | LoRA (r=16, alpha=16) |
| Training Epochs | 3 |
| Learning Rate | 2e-4 |
| Training Loss | 1.37 → 0.47 |
| Framework | Unsloth + TRL |
| Hardware | Google Colab T4 GPU |

## Usage

```python
from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "Almanships/Phi3-UrduInstruct",
    max_seq_length = 2048,
    dtype = None,
    load_in_4bit = True,
)

FastLanguageModel.for_inference(model)

messages = [
    {"role": "user", "content": 
     "اس جملے کا انگریزی میں ترجمہ کریں\nپاکستان ایک خوبصورت ملک ہے"}
]

inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt"
).to("cuda")

outputs = model.generate(input_ids=inputs, max_new_tokens=128)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Example Outputs

**Translation:**
- Input: `پاکستان ایک خوبصورت ملک ہے`
- Output: `Pakistan is a beautiful country`

**Grammar Correction:**
- Input: `وہ گیا بازار آج`
- Output: `وہ آج بازار گیا`

**Question Answering:**
- Input: `پاکستان کا دارالحکومت کون سا ہے؟`
- Output: `پاکستان کا دارالحکومت اسلام آباد ہے`

## Limitations

- Trained on 578 examples — larger dataset would 
  improve performance
- Evaluation is currently qualitative; 
  formal benchmarks pending
- Best performance on the 6 trained task categories

## Future Work

- Expand dataset to 2000+ examples
- Add formal evaluation benchmarks for Urdu NLP
- Extend to Punjabi language instruction tuning
- Compare against other multilingual models on Urdu tasks

## Citation

If you use this model, please cite:

```
@misc{phi3-urduinstruct-2026,
  author = {Almanships},
  title = {Phi3-UrduInstruct: Instruction Tuning of 
           Phi-3 for Urdu Language},
  year = {2026},
  publisher = {HuggingFace},
  url = {https://huggingface.co/Almanships/Phi3-UrduInstruct}
}
```