Khurram123/Qwen-Urdu-Shaheen-7B-Instruct-v1

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ModelHub XC d12ef646a6 初始化项目，由ModelHub XC社区提供模型

Model: Khurram123/Qwen-Urdu-Shaheen-7B-Instruct-v1
Source: Original Platform

2026-05-06 21:06:56 +08:00

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🦅 Qwen-Urdu-Shaheen-7B-Instruct (v1.0) 🇵🇰

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.

Built on the Qwen 2.5 7B Instruct architecture using Unsloth, this model delivers high-performance inference with deep cultural and linguistic nuances.

🌟 Key Highlights

Massive Scale: Fine-tuned on 1.83M curated Urdu records.
Literary Depth: Specialized in the philosophy of Allama Iqbal, the poetry of Ghalib, and Ahmed Faraz.
Instruction Master: Optimized with the Alif-Instruct dataset for precise Urdu command following.
Modern Context: Integrated with the Lughat News Corpus for contemporary vocabulary and news synthesis.
OCR Synergized: Trained to process and generate couplets derived from Urdu Poetry OCR datasets.

📊 Dataset Composition

The model was trained on a multi-domain Urdu corpus to ensure versatility:

Category	Dataset Source	Description
Literature	Iqbaliyat & Ghazal Bank	Classical and contemporary poetry analysis.
Instruction	Alif-Instruct	Multi-turn Urdu dialogues and logic tasks.
Current Affairs	Lughat News	Modern Urdu prose and media vocabulary.
Specialized	Urdu-Poetry-OCR	Structural understanding of poetic couplets.

🛠️ Technical Specifications

Base Model: unsloth/Qwen2.5-7B-Instruct-bnb-4bit
Architecture: Causal Language Model (Transformer)
Parameters: 7 Billion
Training Tool: Unsloth (2x faster finetuning)
Hardware: NVIDIA GeForce RTX 4060 Ti 16GB
Quantization: 4-bit (bitsandbytes)
Checkpoint: 4500 Steps

💎 Shaheen Highlights

Feature	Capability
Dataset Size	1.83 Million Urdu Rows
Optimization	Unsloth (4-bit LoRA)
Primary Focus	Iqbaliyat & Urdu Prose
OCR Support	Specialized for Nastaliq script couplets

🚀 Quick Start (Inference)

from unsloth import FastLanguageModel
import torch

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "Khurram123/Qwen-Urdu-Shaheen-7B-Instruct-v1",
    max_seq_length = 2048,
    load_in_4bit = True,
)
FastLanguageModel.for_inference(model)

# Sample Prompt
prompt = "علامہ اقبال کے فلسفہء خودی کا خلاصہ پیش کریں۔"
inputs = tokenizer([f"<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n"], return_tensors="pt").to("cuda")

outputs = model.generate(**inputs, max_new_tokens=300, temperature=0.7)
print(tokenizer.batch_decode(outputs)[0])

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