codegen-350M-mono-ruby

CodeGen (CodeGen-Mono 350M LoRa Ruby)

Model description

CodeGen LoRa Ruby is a family of autoregressive language models fine-tuned using LoRa on Different Programming Langauges.

Training data

<!-- https://huggingface.co/datasets/ammarnasr/the-stack-ruby-clean -->

This model was fine-tuned on the cleaned Ruby subset from TheStack Avilable here. The data consists of 1 Million Ruby code files.

Training procedure

This model was fine-tuned using LoRa on 1 T4 GPU. The model was trained for 10,000 steps with batch size of 4. The model was trained using causal language modeling loss.

Evaluation results

We evaluate our models on the MultiPle-E bencchmark. The model achieves 8.9 Pass@10 Rate.

Intended Use and Limitations

However, the model is intended for and best at program synthesis, that is, generating executable code given English prompts, where the prompts should be in the form of a comment string. The model can complete partially-generated code in Ruby and Python.

How to use

This model can be easily loaded using the AutoModelForCausalLM functionality:

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftConfig, PeftModel

model_name = "ammarnasr/codegen-350M-mono-ruby"
peft_config = PeftConfig.from_pretrained(model_name)

tokenizer = AutoTokenizer.from_pretrained(peft_config.base_model_name_or_path)

model = AutoModelForCausalLM.from_pretrained(peft_config.base_model_name_or_path)
model = PeftModel.from_pretrained(model, model_name)

model.print_trainable_parameters()

text = "def hello_world"

input_ids = tokenizer.encode(text, return_tensors="pt")
generated_ids = model.generate(input_ids=input_ids, max_length=100)
print('Generated: \n')
print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))

BibTeX entry and citation info

@article{Nijkamp2022ACP,
  title={A Conversational Paradigm for Program Synthesis},
  author={Nijkamp, Erik and Pang, Bo and Hayashi, Hiroaki and Tu, Lifu and Wang, Huan and Zhou, Yingbo and Savarese, Silvio and Xiong, Caiming},
  journal={arXiv preprint},
  year={2022}
}