43 lines
1.6 KiB
Markdown
43 lines
1.6 KiB
Markdown
---
|
|
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)) |