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Model: URajinda/ShweYon-Qwen2.5-Burmese-1.5B-v1.2
Source: Original Platform

README.md

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+---
+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))