Lughaat 1.0 8B Instruct

🚀 Lughaat-1.0-8B-Instruct

Lughaat-1.0-8B-Instruct is an advanced Urdu language model, built on the Llama 3.1 8B architecture. It is trained on a large Urdu dataset, enabling superior performance in Urdu language tasks compared to similar models.

🚀 Quick Start

Lughaat-1.0-8B-Instruct is an Urdu language model developed by Muhammad Noman, based on the architecture of Llama 3.1 8B. It is trained on the muhammadnoman76/lughaat-urdu-dataset-llm, the largest Urdu dataset compiled by Muhammad Noman. This allows it to outperform competitors such as Qwen-2.5-7b, Mistral 7B, and Alif 8B in Urdu language tasks.

✨ Features

• Multilingual Support: Supports both Urdu (ur) and English (en).
• Multiple Usage Methods: Can be used via Unsloth, Hugging Face Pipeline, or direct loading with Transformers.
• Superior Performance: Outperforms similar-sized competitors in various Urdu language tasks.

📦 Installation

This model is available on Hugging Face and can be installed and used in multiple ways:

Method 1: Using Unsloth for Optimized Inference

from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "muhammadnoman76/Lughaat-1.0-8B-Instruct", 
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)

FastLanguageModel.for_inference(model)

# Define the prompt template for Urdu instructions
lughaat_prompt = """نیچے ایک ہدایت ہے جو کسی کام کی تفصیل بیان کرتی ہے، جس کے ساتھ ایک ان پٹ دیا گیا ہے جو مزید سندات فراہم کرتا ہے۔ تھوڑا وقت لیکر ایک جواب لکھیں جو درست طریقے سے درخواست مکمل کریں
### Instruction:
{}
### Input:
{}
### Response:
{}"""

# Example usage
inputs = tokenizer(
[
    lughaat_prompt.format(
        "قائد اعظم کون ہے؟", 
        "", 
        "", 
    )
], return_tensors = "pt").to("cuda")

# Generate response with streaming
from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
outputs = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)

Method 2: Using Hugging Face Pipeline

from transformers import pipeline

pipe = pipeline("text-generation", model="muhammadnoman76/Lughaat-1.0-8B-Instruct")
result = pipe("نیچے ایک ہدایت ہے جو کسی کام کی تفصیل بیان کرتی ہے، جس کے ساتھ ایک ان پٹ دیا گیا ہے جو مزید سندات فراہم کرتا ہے۔ تھوڑا وقت لیکر ایک جواب لکھیں جو درست طریقے سے درخواست مکمل کریں\n### Instruction: قائد اعظم کون ہے؟\n### Input:\n### Response:")

Method 3: Direct Loading with Transformers

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("muhammadnoman76/Lughaat-1.0-8B-Instruct")
model = AutoModelForCausalLM.from_pretrained("muhammadnoman76/Lughaat-1.0-8B-Instruct")

# Process input
prompt = """نیچے ایک ہدایت ہے جو کسی کام کی تفصیل بیان کرتی ہے، جس کے ساتھ ایک ان پٹ دیا گیا ہے جو مزید سندات فراہم کرتا ہے۔ تھوڑا وقت لیکر ایک جواب لکھیں جو درست طریقے سے درخواست مکمل کریں
### Instruction:
قائد اعظم کون ہے؟
### Input:

### Response:
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=128)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

💻 Usage Examples

Basic Usage

Use the provided installation methods to load the model and generate text according to the prompt format.

Advanced Usage

For more complex tasks, adjust the parameters such as max_new_tokens and dtype to optimize performance.

📚 Documentation

Prompt Format

For optimal results, use the following prompt format:

نیچے ایک ہدایت ہے جو کسی کام کی تفصیل بیان کرتی ہے، جس کے ساتھ ایک ان پٹ دیا گیا ہے جو مزید سندات فراہم کرتا ہے۔ تھوڑا وقت لیکر ایک جواب لکھیں جو درست طریقے سے درخواست مکمل کریں
### Instruction:
[Your instruction in Urdu]
### Input:
[Additional context or input - can be empty]
### Response:

Model Capabilities

Lughaat-1.0-8B-Instruct is specifically designed for Urdu language processing tasks including:

• Question answering
• Text generation
• Summarization
• Translation
• Content creation
• Conversational AI in Urdu

Hardware Requirements

• For optimal performance, a CUDA-compatible GPU is recommended.
• Minimum of 16GB VRAM for full precision inference.
• 8GB VRAM when using 4-bit quantization.

🔧 Technical Details

Model Details

Performance Benchmarks

Lughaat-1.0-8B-Instruct outperforms similar-sized competitors in Urdu language tasks, including:

• Qwen-2.5-7b
• Mistral 7B
• Alif 8B

Benchmark Results: Lughaat-1.0-8B-Instruct vs. Competitors

Lughaat-1.0-8B-Instruct Performance Evaluation

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Key Findings

• Lughaat-1.0-8B-Instruct achieves the highest scores across all evaluation categories, with an average performance of 91.4%, demonstrating its superior capabilities in Urdu language understanding and generation.
• The model shows particularly strong performance in Translation (94.2%) and Generation (93.5%), outperforming the previous best model (Alif) by 4.2 and 3.5 percentage points respectively.
• In Ethics and Reasoning categories, Lughaat maintains a significant lead over competitors, showing its balanced performance across different language tasks.
• Compared to larger models like Gemma-2-9b-it, Lughaat-1.0-8B-Instruct delivers better results despite having similar or smaller parameter counts, demonstrating the effectiveness of the specialized training dataset and methodology.
• The performance gap is most significant when compared to general-purpose models like Llama-3-8b-Instruct, highlighting the benefits of language-specific optimization.

📄 License

Please refer to the model card on Hugging Face for the most up-to-date license information.

Citation

If you use this model in your research or applications, please cite it as follows:

@misc{noman2025lughaat,
  author = {Muhammad Noman},
  title = {Lughaat-1.0-8B-Instruct: An Advanced Urdu Language Model},
  year = {2025},
  publisher = {Hugging Face},
  journal = {Hugging Face Model Hub},
  howpublished = {\url{https://huggingface.co/muhammadnoman76/Lughaat-1.0-8B-Instruct}}
}

Acknowledgements

Special thanks to Muhammad Noman for developing this model and compiling the extensive Urdu dataset that powers it.

Contact & Support

For questions, feedback, or collaboration opportunities:

• Email: muhammadnomanshafiq76@gmail.com
• LinkedIn: https://www.linkedin.com/in/muhammad-noman76/