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Model: enstazao/Qalb-1.0-8B-Instruct
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README.md

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+---
+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}
+}
+```
+
+
+
+

config.json

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+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "dtype": "bfloat16",
+  "eos_token_id": 128001,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "pad_token_id": 128004,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 8.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": false,
+  "transformers_version": "4.57.3",
+  "unsloth_fixed": true,
+  "unsloth_version": "2026.1.2",
+  "use_cache": true,
+  "vocab_size": 128256
+}

generation_config.json

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+  "_from_model_config": true,
+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": 128001,
+  "max_length": 131072,
+  "pad_token_id": 128004,
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "4.57.3"
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