add instruction

README.md

@@ -112,3 +112,36 @@ datasets:
 - gmnlp/tico19
 - tatoeba
 ---
+
+# 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), 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 file for the moment.
+
+```
+from transformers import M2M100ForConditionalGeneration
+from tokenization_small100 import SMALL100Tokenizer
+
+hi_text = "जीवन एक चॉकलेट बॉक्स की तरह है।"
+chinese_text = "生活就像一盒巧克力。"
+
+model = M2M100ForConditionalGeneration.from_pretrained("alirezamsh/small100")
+tokenizer = SMALL100Tokenizer.from_pretrained("alirezamsh/small100")
+
+# translate Hindi to French
+tokenizer.tgt_lang = "fr"
+encoded_hi = tokenizer(hi_text, return_tensors="pt")
+generated_tokens = model.generate(**encoded_hi)
+tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
+# => "La vie est comme une boîte de chocolat."
+
+# translate Chinese to English
+tokenizer.tgt_lang = "en"
+encoded_zh = tokenizer(chinese_text, return_tensors="pt")
+generated_tokens = model.generate(**encoded_zh)
+tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
+# => "Life is like a box of chocolate."
+```
+
+Please refer to [original repository](https://github.com/alirezamshi/small100) for further details.