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Model: Khurram123/Urdu-Llama-3.2-3B-Instruct-v1
Source: Original Platform

README.md

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+---
+language:
+- ur
+license: apache-2.0
+base_model: unsloth/Llama-3.2-3B-Instruct
+tags:
+- urdu
+- instruction-finetuning
+- unsloth
+- llama-3.2
+- khurramcoder
+datasets:
+- large-traversaal/urdu-instruct
+metrics:
+- loss
+model-index:
+- name: Urdu-Llama-3.2-3B-Instruct-v1
+  results: []
+---
+
+# Urdu-Llama-3.2-3B-Instruct-v1
+
+Developed by **Khurram Pervez (Khurramcoder)**, this model is a fine-tuned version of Meta's Llama-3.2-3B-Instruct, specifically optimized for high-quality Urdu instruction following and generation.
+
+## Model Highlights
+- **Native Urdu Reasoning:** Trained on the `large-traversaal/urdu-instruct` dataset (51.7k rows), enabling the model to handle translation, creative writing, and QA tasks with cultural nuance.
+- **Efficient Architecture:** Fine-tuned using **Unsloth** and QLoRA on an NVIDIA RTX 4060 Ti, making it a powerful yet lightweight 3B parameter model.
+- **Optimized for 2026:** Uses the latest Llama 3.2 multilingual tokenizer for better Urdu script handling.
+
+## How to Use
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "Khurram123/Urdu-Llama-3.2-3B-Instruct-v1"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")
+
+instruction = "مصنوعی ذہانت کے مستقبل پر ایک مختصر نوٹ لکھیں۔"
+prompt = f"### ہدایت:\n{instruction}\n\n### جواب:\n"
+
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+outputs = model.generate(**inputs, max_new_tokens=256)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))