Create README.md

README.md

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+---
+license: apache-2.0
+language:
+- pt
+- vmw
+datasets:
+- LIACC/Emakhuwa-Portuguese-News-MT
+base_model:
+- facebook/nllb-200-distilled-600M
+pipeline_tag: translation
+---
+
+# CTranslate2 NLLB-200 Translation Example
+
+This guide demonstrates how to use NLLB-finetuned model for bilingual translation between Portuguese (`por_Latn`) and a target language (`vmw_Latn`).
+
+## Prerequisites
+
+- Install required packages:
+```bash
+pip install transformers torch
+```
+
+## Inference
+```python
+from transformers import AutoModelForSeq2SeqLM, NllbTokenizer, AutoTokenizer
+import torch
+
+src_lang="por_Latn"
+tgt_lang="vmw_Latn"
+text="Olá mundo das língua!"
+
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+
+model_name="felerminoali/nllb_bilingual_pt-vmw_65k"
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name).to(device)
+tokenizer = NllbTokenizer.from_pretrained(model_name)
+
+tokenizer.src_lang = src_lang
+tokenizer.tgt_lang = tgt_lang
+
+inputs = tokenizer(
+    text, return_tensors='pt', padding=True, truncation=True, 
+    max_length=1024
+)
+model.eval() # turn off training mode
+result = model.generate(
+    **inputs.to(model.device),
+    forced_bos_token_id=tokenizer.convert_tokens_to_ids(tgt_lang)
+)
+
+print(tokenizer.batch_decode(result, skip_special_tokens=True)[0])
+
+```