---
base_model: unsloth/Meta-Llama-3.1-8B
library_name: transformers
tags:
- unsloth
- urdu
- llama-3.1
- instruct
- fine-tuned
- nlp
license: apache-2.0
language:
- ur
- en
---

# Qalb-1.0-8B-Instruct (Urdu Llama 3.1)

<div align="center">

![Unsloth](https://img.shields.io/badge/Made%20with-Unsloth-blue?style=for-the-badge)
![Urdu](https://img.shields.io/badge/Language-Urdu-green?style=for-the-badge)
![License](https://img.shields.io/badge/License-Apache_2.0-yellow?style=for-the-badge)

</div>

**Qalb-1.0-8B-Instruct** is a state-of-the-art Urdu language model designed to bridge the gap in low-resource language processing. Built on the powerful **Llama-3.1-8B** architecture, Qalb has been rigorously adapted for the Urdu language through a two-stage process: **Continued Pre-training** on a massive Urdu corpus of 1.97 billion tokens followed by **Supervised Fine-Tuning** for instruction following.

Unlike general multilingual models that struggle with Urdu grammar and cultural nuance, **Qalb** delivers fluent, culturally accurate, and context-aware responses.

## 🌟 Key Features

* **State-of-the-Art Performance:** Outperforms previous best models (Alif-1.0 and LLaMA-3.1 Base) on 6 out of 7 benchmarks.
* **Deep Urdu Understanding:** Pre-trained on a diverse mix of news, literature, government documents, and social media to capture the depth of the language.
* **Ethical & Safe:** Fine-tuned to provide helpful, harmless, and honest assistants, refusing to generate toxic or misleading content.
* **Reasoning Capable:** Excellent performance on logical reasoning, mathematical word problems, and commonsense tasks in Urdu.
* **Bilingual Proficiency:** Retains strong English capabilities while excelling in Urdu, making it ideal for translation and code-switching tasks.

## 📊 Performance Benchmarks

Qalb establishes a new standard for Urdu LLMs, achieving an **Overall Score of 90.34**. It significantly outperforms the base model and the previous state-of-the-art.

### 🏆 Comparison vs. SOTA Models

| Task | **Qalb (Ours)** | **Alif-1.0-Instruct** | **LLaMA-3.1-8B-Instruct** |
| :--- | :---: | :---: | :---: |
| **Overall Score** | **90.34** | 87.1 | 45.7 |
| **Translation** | **94.41** | 89.3 | 58.9 |
| **Classification** | **96.38** | 93.9 | 61.4 |
| **Sentiment Analysis** | **95.79** | 94.3 | 54.3 |
| **Ethics** | **90.83** | 85.7 | 27.3 |
| **Reasoning** | **88.59** | 83.5 | 45.6 |
| **QA (Question Answering)**| **80.40** | 73.8 | 30.5 |
| **Generation** | 85.97 | **90.2** | 42.8 |

*> **Note:** Scores are on a 0-100 scale. Qalb outperforms the previous best model (Alif) in **6 out of 7** categories.*

## 🚀 How to Use

### Google COlab
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1SQ_OaPhr1Q130FDho89zvughfRxJqdoF?usp=sharing)


### Method 1: Using Unsloth (Recommended - Fast & Efficient)
The easiest way to run Qalb is using the Unsloth library, which provides 2x faster inference.

```python
from unsloth import FastLanguageModel
import torch

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "enstazao/Qalb-1.0-8B-Instruct",
    max_seq_length = 2048,
    dtype = None,
    load_in_4bit = True, # <--- Currently set to use 4-bit quantization
)
FastLanguageModel.for_inference(model)


urdu_system_prompt = "آپ ایک مددگار اور بے ضرر مصنوعی ذہانت کے اسسٹنٹ ہیں۔ آپ اردو میں سوالات کے درست جوابات دیتے ہیں۔"

questions = [
    "پاکستان کا قومی کھیل کیا ہے؟",                         
    "لاہور شہر کیوں مشہور ہے؟ مختصر وضاحت کریں۔",
    "سوال: لیاقت علی خان کون تھے؟",
    "کراچی کو روشنیوں کا شہر کیوں کہا جاتا ہے؟",             
    "انگریزی میں ترجمہ کریں: 'محنت کامیابی کی کنجی ہے۔'"
]

print("🚀 Starting Batch Generation...\n")


for user_input in questions:
    print(f"🔹 Question: {user_input}")

    # Manually Format Prompt (Llama-3 Style)
    prompt = f"""<|begin_of_text|><|start_header_id|>system<|end_header_id|>

{urdu_system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>

{user_input}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
"""

    inputs = tokenizer([prompt], return_tensors = "pt").to("cuda")

    outputs = model.generate(
        **inputs,
        max_new_tokens = 256,
        temperature = 0.1,
        top_p = 0.9,
        repetition_penalty = 1.1,
        do_sample = True,
        eos_token_id = [tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<|eot_id|>")]
    )

    response = tokenizer.decode(outputs[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True)
    
    print(f"✅ Answer: {response}")
    print("-" * 50)
```




## Method 2: Using Hugging Face Transformers
Compatible with standard transformers if Unsloth is not available.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
import torch


model_name = "enstazao/Qalb-1.0-8B-Instruct"
urdu_system_prompt = "آپ ایک مددگار اور بے ضرر مصنوعی ذہانت کے اسسٹنٹ ہیں۔ آپ اردو میں سوالات کے درست جوابات دیتے ہیں۔"


bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)



print("⏳ Loading model in 4-bit...")
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    quantization_config=bnb_config, # <--- Apply 4-bit here
    device_map="auto"               # <--- Required for quantization
)

terminators = [
    tokenizer.eos_token_id,
    tokenizer.convert_tokens_to_ids("<|eot_id|>")
]


questions = [
    "پاکستان کا قومی کھیل کیا ہے؟",                         
    "لاہور شہر کیوں مشہور ہے؟ مختصر وضاحت کریں۔",            
    "سوال: لیاقت علی خان کون تھے؟",                     
    "سوال: اسلام آباد شہر کے بارے میں بتائیں۔",  
    "انگریزی میں ترجمہ کریں: 'محنت کامیابی کی کنجی ہے۔'"
]

print("Model Loaded. Starting Generation...\n")

# 5. Loop through questions
for user_input in questions:
    print(f"🔹 Question: {user_input}")
    
    prompt = f"""<|begin_of_text|><|start_header_id|>system<|end_header_id|>

{urdu_system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>

{user_input}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
"""

    input_ids = tokenizer([prompt], return_tensors="pt").to("cuda")

    outputs = model.generate(
        **input_ids,
        max_new_tokens = 256,
        temperature = 0.1,
        top_p = 0.9,
        repetition_penalty = 1.1,
        do_sample = True,
        eos_token_id = terminators
    )

    response = tokenizer.decode(outputs[0][input_ids['input_ids'].shape[1]:], skip_special_tokens=True)
    
    print(f"✅ Answer: {response}")
    print("-" * 50)
```

## Limitation & Bias
While Qalb has been trained to be helpful and harmless, it may still reflect biases present in the training data. Users should fact-check critical information, especially in medical, legal, or religious contexts.

## Citation
If you use QALB in your research, please cite:

```
@article{qalb2025,
  title={Qalb: Largest State-of-the-Art Urdu Large Language Model for 230M Speakers with Systematic Continued Pre-training},
  author={Hassan, Muhammad Taimoor and Ahmed, Jawad and Awais, Muhammad},
  journal={arXiv preprint arXiv:2601.08141},
  year={2026},
  eprint={2601.08141},
  archivePrefix={arXiv},
  primaryClass={cs.CL},
  url={[https://arxiv.org/abs/2601.08141](https://arxiv.org/abs/2601.08141)},
  doi={10.48550/arXiv.2601.08141}
}
```