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Khurram123/
Shaheen-3B-Kulliyat-e-Iqbal

Name: Shaheen-3B-Kulliyat-e-Iqbal
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"تو شاہیں ہے، پرواز ہے کام تیرا"
— علامہ اقبال کے کلام اور فلسفے پر مبنی پہلا سپیشلائزڈ لسانی ماڈل

Shaheen Iqbal Model Logo

🦅 Shaheen-Qwen2.5-3B-Kulliyat-e-Iqbal (v1.0) 🇵🇰

Shaheen-Qwen2.5-3B is a specialized Large Language Model fine-tuned on the complete poetic works of Allama Muhammad Iqbal. Built on the Qwen 2.5 3B Instruct architecture, this model is designed to interpret, explain, and contextualize the philosophical depth of Iqbaliyat in both Urdu and Persian.

Using the Kulliyat-e-Iqbal Shaheen Dataset (11,659 records), this model bridges the gap between classical wisdom and modern conversational AI.

🌟 Model Highlights

Specialized Knowledge: Deeply trained on 11 major Urdu and Persian books of Allama Iqbal.
Bilingual Proficiency: Capable of understanding and explaining Persian (Farsi) couplets in simple Urdu.
Philosophical Insight: Optimized to discuss core concepts like Khudi (Selfhood), Ishq (Divine Love), and Shaheen (The Eagle).
Lightweight & Efficient: 3 Billion parameters ensure lightning-fast inference on consumer-grade hardware (like RTX 4060 Ti).
Optimization: Fine-tuned using Unsloth with 4-bit LoRA for maximum performance with minimum VRAM footprint.

📊 Training Details

Base Model: unsloth/qwen2.5-3b-instruct-bnb-4bit
Dataset Size: 11,659 Rows (Instruction-Response Pairs)
Epochs: 1.37
Final Train Loss: 1.39
Hardware: NVIDIA GeForce RTX 4060 Ti (16GB)
Software: Ubuntu Linux + Unsloth AI

📚 Dataset Composition

The model has "read" and analyzed the entire poetic corpus:

Language	Primary Books Included
Urdu	Bang-e-Dara, Bal-e-Jibreel, Zarb-e-Kaleem, Armaghan-e-Hijaz (Urdu)
Persian	Asrar-e-Khudi, Rumuz-e-Bekhudi, Payam-e-Mashriq, Zabur-e-Ajam, Javid Nama, Pas Cha Bayad Kard

🚀 How to Use (Inference)

Via Transformers & Unsloth

python

from unsloth import FastLanguageModel
import torch

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "Khurram123/Shaheen-3B-Kulliyat-e-Iqbal",
    max_seq_length = 2048,
    load_in_4bit = True,
)
FastLanguageModel.for_inference(model)

prompt = "علامہ اقبال کے تصورِ خودی کا پیغام کیا ہے؟"
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=250, temperature=0.5)
print(tokenizer.batch_decode(outputs)[0])