101 lines
3.6 KiB
Markdown
101 lines
3.6 KiB
Markdown
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---
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license: apache-2.0
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base_model: Qwen/Qwen2.5-7B-Instruct
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datasets:
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- anuragshas/ur_opus100_processed_cv9
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- mahwizzzz/urdu_alpaca_yc_filtered
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- large-traversaal/urdu-instruct
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- muhammadnoman76/lughaat-urdu-dataset-llm
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tags:
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- urdu
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- nlp
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- qwen
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- unsloth
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- instruct
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- tts-ready
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- ocr-optimized
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- mega-dataset
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language:
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- ur
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metrics:
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- loss
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library_name: transformers
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pipeline_tag: text-generation
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model_name: Qwen-Urdu-Shaheen-7B-Instruct-v1
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---
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<p align="center">
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<br>
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<b style="font-size: 24px;">"شاہین کا جہاں اور، کرگس کا جہاں اور..."</b> <br>
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<b style="font-size: 18px;">— علامہ اقبال کے افکار اور اردو ادب کی ترویج کے لیے ایک جدید لسانی ماڈل</b>
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</p>
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<p align="center">
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<img src="https://huggingface.co/Khurram123/Qwen-Urdu-Shaheen-7B-Instruct-v1/resolve/main/images.jpeg" width="300" alt="Qwen-Urdu-Shaheen Logo">
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</p>
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<h1 align="center">🦅 Qwen-Urdu-Shaheen-7B-Instruct (v1.0) 🇵🇰</h1>
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**Qwen-Urdu-Shaheen** is a state-of-the-art Urdu Language Model fine-tuned on a massive **1.83 Million row** corpus. It is designed to bridge the gap between classical Urdu intellectual heritage and modern conversational AI.
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Built on the **Qwen 2.5 7B Instruct** architecture using **Unsloth**, this model delivers high-performance inference with deep cultural and linguistic nuances.
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---
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## 🌟 Key Highlights
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- **Massive Scale:** Fine-tuned on 1.83M curated Urdu records.
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- **Literary Depth:** Specialized in the philosophy of **Allama Iqbal**, the poetry of **Ghalib**, and **Ahmed Faraz**.
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- **Instruction Master:** Optimized with the **Alif-Instruct** dataset for precise Urdu command following.
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- **Modern Context:** Integrated with the **Lughat News Corpus** for contemporary vocabulary and news synthesis.
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- **OCR Synergized:** Trained to process and generate couplets derived from Urdu Poetry OCR datasets.
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---
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## 📊 Dataset Composition
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The model was trained on a multi-domain Urdu corpus to ensure versatility:
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| Category | Dataset Source | Description |
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| :--- | :--- | :--- |
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| **Literature** | Iqbaliyat & Ghazal Bank | Classical and contemporary poetry analysis. |
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| **Instruction** | Alif-Instruct | Multi-turn Urdu dialogues and logic tasks. |
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| **Current Affairs** | Lughat News | Modern Urdu prose and media vocabulary. |
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| **Specialized** | Urdu-Poetry-OCR | Structural understanding of poetic couplets. |
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---
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## 🛠️ Technical Specifications
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- **Base Model:** `unsloth/Qwen2.5-7B-Instruct-bnb-4bit`
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- **Architecture:** Causal Language Model (Transformer)
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- **Parameters:** 7 Billion
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- **Training Tool:** Unsloth (2x faster finetuning)
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- **Hardware:** NVIDIA GeForce RTX 4060 Ti 16GB
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- **Quantization:** 4-bit (bitsandbytes)
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- **Checkpoint:** 4500 Steps
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---
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### 💎 Shaheen Highlights
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| Feature | Capability |
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| :--- | :--- |
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| **Dataset Size** | 1.83 Million Urdu Rows |
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| **Optimization** | Unsloth (4-bit LoRA) |
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| **Primary Focus** | Iqbaliyat & Urdu Prose |
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| **OCR Support** | Specialized for Nastaliq script couplets |
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## 🚀 Quick Start (Inference)
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```python
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from unsloth import FastLanguageModel
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import torch
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model, tokenizer = FastLanguageModel.from_pretrained(
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model_name = "Khurram123/Qwen-Urdu-Shaheen-7B-Instruct-v1",
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max_seq_length = 2048,
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load_in_4bit = True,
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)
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FastLanguageModel.for_inference(model)
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# Sample Prompt
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prompt = "علامہ اقبال کے فلسفہء خودی کا خلاصہ پیش کریں۔"
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inputs = tokenizer([f"<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n"], return_tensors="pt").to("cuda")
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outputs = model.generate(**inputs, max_new_tokens=300, temperature=0.7)
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print(tokenizer.batch_decode(outputs)[0])
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