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ammarnasr/
codegen-350M-mono-rust

Name: codegen-350M-mono-rust
Availability: InStock

adapter-transformers

code

text-generation

ammarnasr/the-stack-rust-clean

mit

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CodeGen (CodeGen-Mono 350M LoRa Rust)

Model description

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

Training data

This model was fine-tuned on the cleaned Rust subset from TheStack Avilable here. The data consists of 1 Million Rust 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 Rust and Python.

How to use

This model can be easily loaded using the AutoModelForCausalLM functionality:

python

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

model_name = "ammarnasr/codegen-350M-mono-rust"
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 = "fn 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

bibtex

@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}
}