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Model: muhammadnoman76/Lughaat-1.0-8B-Instruct Source: Original Platform
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README.md
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README.md
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---
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base_model:
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- meta-llama/Llama-3.1-8B-Instruct
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tags:
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- text-generation-inference
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- transformers
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- llama
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license: apache-2.0
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language:
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- ur
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- en
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datasets:
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- muhammadnoman76/lughaat-urdu-dataset-llm
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metrics:
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- accuracy
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- bleu
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library_name: transformers
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---
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# Lughaat-1.0-8B-Instruct
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## Overview
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Lughaat-1.0-8B-Instruct is an Urdu language model developed by Muhammad Noman, built on the architecture of Llama 3.1 8B. This model is specifically trained on `muhammadnoman76/lughaat-urdu-dataset-llm`, the largest Urdu dataset compiled by Muhammad Noman, enabling it to outperform competitors like Qwen-2.5-7b, Mistral 7B, and Alif 8B models in Urdu language tasks.
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## Model Details
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- **Model Name**: Lughaat-1.0-8B-Instruct
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- **Architecture**: Based on Llama 3.1 8B
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- **Developer**: Muhammad Noman
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- **Language**: Urdu
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- **Training Dataset**: muhammadnoman76/lughaat-urdu-dataset-llm
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- **Contact**:
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- Email: muhammadnomanshafiq76@gmail.com
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- LinkedIn: https://www.linkedin.com/in/muhammad-noman76/
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## Installation & Usage
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This model is available on Hugging Face and can be used in multiple ways:
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### Method 1: Using Unsloth for Optimized Inference
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```python
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from unsloth import FastLanguageModel
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model, tokenizer = FastLanguageModel.from_pretrained(
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model_name = "muhammadnoman76/Lughaat-1.0-8B-Instruct",
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max_seq_length = max_seq_length,
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dtype = dtype,
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load_in_4bit = load_in_4bit,
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)
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FastLanguageModel.for_inference(model)
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# Define the prompt template for Urdu instructions
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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
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### Instruction:
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{}
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### Input:
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{}
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### Response:
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{}"""
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# Example usage
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inputs = tokenizer(
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[
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lughaat_prompt.format(
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"قائد اعظم کون ہے؟",
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"",
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"",
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)
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], return_tensors = "pt").to("cuda")
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# Generate response with streaming
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from transformers import TextStreamer
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text_streamer = TextStreamer(tokenizer)
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outputs = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)
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```
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### Method 2: Using Hugging Face Pipeline
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```python
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from transformers import pipeline
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pipe = pipeline("text-generation", model="muhammadnoman76/Lughaat-1.0-8B-Instruct")
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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:")
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```
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### Method 3: Direct Loading with Transformers
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("muhammadnoman76/Lughaat-1.0-8B-Instruct")
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model = AutoModelForCausalLM.from_pretrained("muhammadnoman76/Lughaat-1.0-8B-Instruct")
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# Process input
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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
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### Instruction:
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قائد اعظم کون ہے؟
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### Input:
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### Response:
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"""
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inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
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outputs = model.generate(**inputs, max_new_tokens=128)
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response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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print(response)
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```
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## Prompt Format
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For optimal results, use the following prompt format:
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```
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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
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### Instruction:
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[Your instruction in Urdu]
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### Input:
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[Additional context or input - can be empty]
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### Response:
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```
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## Model Capabilities
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Lughaat-1.0-8B-Instruct is specifically designed for Urdu language processing tasks including:
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- Question answering
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- Text generation
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- Summarization
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- Translation
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- Content creation
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- Conversational AI in Urdu
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## Hardware Requirements
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- For optimal performance, a CUDA-compatible GPU is recommended
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- Minimum of 16GB VRAM for full precision inference
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- 8GB VRAM when using 4-bit quantization
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## Performance Benchmarks
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Lughaat-1.0-8B-Instruct outperforms similar-sized competitors in Urdu language tasks, including:
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- Qwen-2.5-7b
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- Mistral 7B
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- Alif 8B
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-
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# LLM-as-Judge evaluation on Human Annotated Urdu Dataset
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## Benchmark Results: Lughaat-1.0-8B-Instruct vs. Competitors
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| Category | Lughaat-1.0-8B-Instruct | Alif-1.0-8B-Instruct | Gemma-2-9b-it | Aya expanse 8B | Llama-3-8b-Instruct | Mistral-Nemo-Instruct-2407 | Qwen2.5-7B-Instruct |
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|----------|-------------------------|---------------------|---------------|----------------|---------------------|---------------------------|-------------------|
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| Generation | 89.5 | 90.0 | 84.0 | 73.0 | 65.0 | - | - |
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| Translation | 94.2 | 90.0 | 90.0 | - | 65.0 | 79.5 | - |
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| Ethics | 89.7 | 85.5 | 84.0 | 71.5 | 64.0 | - | - |
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| Reasoning | 88.3 | 83.5 | 85.0 | - | - | 79.5 | 72.0 |
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| **Average Score** | **91.4** | **87.3** | **85.8** | **72.3** | **64.7** | **79.5** | **72.0** |
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# Lughaat-1.0-8B-Instruct Performance Evaluation
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*Note: This is a placeholder for the actual graph image that would be created based on the data.*
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### Key Findings
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- **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.
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- 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.
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- In Ethics and Reasoning categories, Lughaat maintains a significant lead over competitors, showing its balanced performance across different language tasks.
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- 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.
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- The performance gap is most significant when compared to general-purpose models like Llama-3-8b-Instruct, highlighting the benefits of language-specific optimization.
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## License & Usage Restrictions
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Please refer to the model card on Hugging Face for the most up-to-date license information.
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## Citation
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If you use this model in your research or applications, please cite it as follows:
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```
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@misc{noman2025lughaat,
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author = {Muhammad Noman},
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title = {Lughaat-1.0-8B-Instruct: An Advanced Urdu Language Model},
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year = {2025},
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publisher = {Hugging Face},
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journal = {Hugging Face Model Hub},
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howpublished = {\url{https://huggingface.co/muhammadnoman76/Lughaat-1.0-8B-Instruct}}
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}
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```
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## Acknowledgements
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Special thanks to Muhammad Noman for developing this model and compiling the extensive Urdu dataset that powers it.
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## Contact & Support
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For questions, feedback, or collaboration opportunities:
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- Email: muhammadnomanshafiq76@gmail.com
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- LinkedIn: https://www.linkedin.com/in/muhammad-noman76/
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