Edit model card

GPT-CSRC

This is a GPT2 774M model trained on the C/C++ code of the top 10,000 most popular packages in Debian, according to the Debian Popularity Contest. The source files were deduplicated using a process similar to the OpenWebText preprocessing (basically a locality-sensitive hash to detect near-duplicates). The model was originally trained using NVIDIA's Megatron-LM but has been converted to Huggingface. Note that the tokenizer is not the standard GPT2 BPE vocab, but one that has been trained for this dataset; the tokenizer is also available from this repository.

The processed dataset (in JSON format) can be found here: csrc_dataset_large.json.gz.

This model was used to generate snippets for the web site This Code Does Not Exist.

Usage

>>> import torch
>>> from transformers import AutoModelForCausalLM, AutoTokenizer
>>> model = AutoModelForCausalLM.from_pretrained("moyix/csrc_774m")
>>> device = torch.device("cuda")
>>> model.to(device)
>>> tokenizer = AutoTokenizer.from_pretrained("moyix/csrc_774m")
>>> prompt = tokenizer.encode('// say hello\nvoid hello() {', return_tensors="pt")
>>> output = model.generate(input_ids=prompt.to(device), max_length=32, num_return_sequences=1, do_sample=True, num_beams=4)
>>> print(tokenizer.decode(output[0].tolist(),clean_up_tokenization_spaces=True))
// say hello
void hello() {
  std::cout << "hello" << std::endl;
}

int main() {
Downloads last month
48
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.