---
language:
- my
license: apache-2.0
base_model: URajinda/Qwen2.5-MM-1.5B-Base
tags:
- burmese
- myanmar
- shweyon
- qwen
- tokenization
library_name: transformers
---

# 🚀 ShweYon-Qwen2.5-Burmese-1.5B-v1.2(Enhanced Burmese LLM)

**ShweYon-v1.2-Base** is a specialized language model based on the Qwen2.5-1.5B architecture, meticulously optimized for the Myanmar (Burmese) language. This version features a significant **Vocabulary Expansion** designed to solve common tokenization inefficiencies in Burmese NLP.


### 📊 Technical Specifications

| Feature | Specification |
| :--- | :--- |
| **Base Architecture** | Qwen2.5-1.5B |
| **New Vocab Size** | 152,858 |
| **Added Tokens** | 1,418 (Cumulative) |
| **Model Size Increase** | ~4.73 MB (+0.40% total weight) |
| **Training Type** | Continual Pre-training (CPT) |
| **Language** | Myanmar (Burmese) |


### 💻 Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "URajinda/ShweYon-Qwen2.5-Burmese-1.5B-v1.2" 

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

text = "မြန်မာနိုင်ငံသည်"
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))