language, license, base_model, tags, datasets, metrics, model-index

language	license	base_model	tags	datasets	metrics	model-index
ur	apache-2.0	unsloth/Llama-3.2-3B-Instruct	urdu instruction-finetuning unsloth llama-3.2 khurramcoder	large-traversaal/urdu-instruct	loss	name results Urdu-Llama-3.2-3B-Instruct-v1

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

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))