Update README.md

README.md

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 license: mit
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+# SMALL-100 Model
+
+SMaLL-100 is a compact and fast massively multilingual machine translation model covering more than 10K language pairs, that achieves competitive results with M2M-100 while being much smaller and faster. It is introduced in [this paper](https://arxiv.org/abs/2210.11621)(accepted to EMNLP2022), and initially released in [this repository](https://github.com/alirezamshi/small100).
+
+The model architecture and config are the same as [M2M-100](https://huggingface.co/facebook/m2m100_418M/tree/main) implementation, but the tokenizer is modified to adjust language codes. So, you should load the tokenizer locally from [tokenization_small100.py](https://huggingface.co/alirezamsh/small100/blob/main/tokenization_small100.py) file for the moment.
+
+**Demo**: https://huggingface.co/spaces/alirezamsh/small100
+
+**Note**: SMALL100Tokenizer requires sentencepiece, so make sure to install it by:
+
+```pip install sentencepiece```
+
+- **Supervised Training**
+
+SMaLL-100 is a seq-to-seq model for the translation task. The input to the model is ```source:[tgt_lang_code] + src_tokens + [EOS]``` and ```target: tgt_tokens + [EOS]```. 
+
+
+# `small-100-th` is the fine tuned model from SMALL-100  
+
+
+# small-100-th inference
+```
+from transformers import M2M100ForConditionalGeneration
+from tokenization_small100 import SMALL100Tokenizer
+from huggingface_hub import notebook_login
+
+notebook_login()
+
+checkpoint = "kimmchii/small-100-th"
+model = M2M100ForConditionalGeneration.from_pretrained(checkpoint)
+tokenizer = SMALL100Tokenizer.from_pretrained(checkpoint)
+
+thai_text = "สวัสดี"
+
+# translate Thai to English
+tokenizer.tgt_lang = "en"
+encoded_th = tokenizer(thai_text, return_tensors="pt")
+generated_tokens = model.generate(**encoded_th)
+tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
+# => "Hello"
+```