43 lines
1.6 KiB
Markdown
43 lines
1.6 KiB
Markdown
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---
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language:
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- ur
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license: apache-2.0
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base_model: unsloth/Llama-3.2-3B-Instruct
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tags:
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- urdu
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- instruction-finetuning
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- unsloth
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- llama-3.2
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- khurramcoder
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datasets:
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- large-traversaal/urdu-instruct
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metrics:
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- loss
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model-index:
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- name: Urdu-Llama-3.2-3B-Instruct-v1
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results: []
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---
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# Urdu-Llama-3.2-3B-Instruct-v1
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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.
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## Model Highlights
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- **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.
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- **Efficient Architecture:** Fine-tuned using **Unsloth** and QLoRA on an NVIDIA RTX 4060 Ti, making it a powerful yet lightweight 3B parameter model.
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- **Optimized for 2026:** Uses the latest Llama 3.2 multilingual tokenizer for better Urdu script handling.
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## How to Use
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "Khurram123/Urdu-Llama-3.2-3B-Instruct-v1"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")
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instruction = "مصنوعی ذہانت کے مستقبل پر ایک مختصر نوٹ لکھیں۔"
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prompt = f"### ہدایت:\n{instruction}\n\n### جواب:\n"
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inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
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outputs = model.generate(**inputs, max_new_tokens=256)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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