90 lines
3.2 KiB
Markdown
90 lines
3.2 KiB
Markdown
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---
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license: apache-2.0
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base_model: Qwen/Qwen2.5-3B-Instruct
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datasets:
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- Khurram123/kulliyat-e-iqbal-shaheen
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tags:
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- iqbaliat
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- urdu-poetry
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- persian-poetry
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- philosophy
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- sufism
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- unsloth
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- qwen
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- shaheen
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- conversational
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language:
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- ur
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- fa
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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: Shaheen-Qwen2.5-3B-Kulliyat-e-Iqbal
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---
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<p align="center">
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<br>
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<b style="font-size: 26px;">"تو شاہیں ہے، پرواز ہے کام تیرا"</b> <br>
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<b style="font-size: 18px;">— علامہ اقبال کے کلام اور فلسفے پر مبنی پہلا سپیشلائزڈ 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/Shaheen-3B-Kulliyat-e-Iqbal/resolve/main/image1.png" width="450" alt="Shaheen Iqbal Model Logo">
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</p>
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<h1 align="center">🦅 Shaheen-Qwen2.5-3B-Kulliyat-e-Iqbal (v1.0) 🇵🇰</h1>
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**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.
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Using the **Kulliyat-e-Iqbal Shaheen Dataset** (11,659 records), this model bridges the gap between classical wisdom and modern conversational AI.
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---
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## 🌟 Model Highlights
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- **Specialized Knowledge:** Deeply trained on 11 major Urdu and Persian books of Allama Iqbal.
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- **Bilingual Proficiency:** Capable of understanding and explaining Persian (Farsi) couplets in simple Urdu.
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- **Philosophical Insight:** Optimized to discuss core concepts like **Khudi (Selfhood)**, **Ishq (Divine Love)**, and **Shaheen (The Eagle)**.
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- **Lightweight & Efficient:** 3 Billion parameters ensure lightning-fast inference on consumer-grade hardware (like RTX 4060 Ti).
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- **Optimization:** Fine-tuned using **Unsloth** with 4-bit LoRA for maximum performance with minimum VRAM footprint.
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---
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## 📊 Training Details
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- **Base Model:** `unsloth/qwen2.5-3b-instruct-bnb-4bit`
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- **Dataset Size:** 11,659 Rows (Instruction-Response Pairs)
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- **Epochs:** 1.37
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- **Final Train Loss:** 1.39
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- **Hardware:** NVIDIA GeForce RTX 4060 Ti (16GB)
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- **Software:** Ubuntu Linux + Unsloth AI
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---
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## 📚 Dataset Composition
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The model has "read" and analyzed the entire poetic corpus:
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| Language | Primary Books Included |
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| :--- | :--- |
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| **Urdu** | Bang-e-Dara, Bal-e-Jibreel, Zarb-e-Kaleem, Armaghan-e-Hijaz (Urdu) |
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| **Persian** | Asrar-e-Khudi, Rumuz-e-Bekhudi, Payam-e-Mashriq, Zabur-e-Ajam, Javid Nama, Pas Cha Bayad Kard |
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---
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## 🚀 How to Use (Inference)
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### Via Transformers & Unsloth
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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/Shaheen-3B-Kulliyat-e-Iqbal",
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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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prompt = "علامہ اقبال کے تصورِ خودی کا پیغام کیا ہے؟"
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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=250, temperature=0.5)
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print(tokenizer.batch_decode(outputs)[0])
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