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README.md

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----
-license: apache-2.0
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+---
+language: en
+license: apache-2.0
+---
+
+# CodeRosetta
+## Pushing the Boundaries of Unsupervised Code Translation for Parallel Programming
+*Repository of CodeRosetta ([Paper](https://arxiv.org/abs/2410.20527), [Website](https://coderosetta.com/)).*  
+
+
+CodeRosetta is an EncoderDecoder translation model. It supports the translation of C++, CUDA, and Fortran. 
+This version of the model is fine-tuned for **C++ to CUDA translation.**
+
+### How to use
+
+```python
+from transformers import AutoTokenizer, EncoderDecoderModel
+
+# Load the CodeRosetta model and tokenizer
+model = EncoderDecoderModel.from_pretrained('CodeRosetta/CodeRosetta_cuda2cpp_ft')
+tokenizer = AutoTokenizer.from_pretrained('CodeRosetta/CodeRosetta_cuda2cpp_ft')
+
+# Encode the input C++ Code
+input_cpp_code = "void add_100 ( int numElements , int * data ) { for ( int idx = 0 ; idx < numElements ; idx ++ ) { data [ idx ] += 100 ; } }"
+input_ids = tokenizer.encode(input_cpp_code, return_tensors="pt")
+
+# Set the start token to <CUDA>
+start_token = "<CUDA>"
+decoder_start_token_id = tokenizer.convert_tokens_to_ids(start_token)
+
+# Generate the CUDA code
+output = model.generate(
+    input_ids=input_ids, 
+    decoder_start_token_id=decoder_start_token_id,
+    max_length=256
+)
+
+# Decode and print the generated output
+generated_code= tokenizer.decode(output[0], skip_special_tokens=True)
+print(generated_code)
+```
+
+### BibTeX 
+
+```bibtex
+@inproceedings{coderosetta:neurips:2024,
+  title = {CodeRosetta: Pushing the Boundaries of Unsupervised Code Translation for Parallel Programming},
+  author = {TehraniJamsaz, Ali and Bhattacharjee, Arijit and Chen, Le and Ahmed, Nesreen K and Yazdanbakhsh, Amir and Jannesari, Ali},
+  booktitle = {NeurIPS},
+  year = {2024},
+}