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--- |
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language: code |
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thumbnail: https://doesnotexist.codes/messlab.png |
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tags: |
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- programming |
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- gpt2 |
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- causal-lm |
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license: cc0-1.0 |
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--- |
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# GPT-CSRC |
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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](https://popcon.debian.org/). 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](https://github.com/nvidia/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. |
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The processed dataset (in JSON format) can be found here: [csrc\_dataset\_large.json.gz](https://moyix.net/~moyix/csrc_dataset_large.json.gz). |
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This model was used to generate snippets for the web site [This Code Does Not Exist](https://doesnotexist.codes/). |
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# Usage |
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``` |
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>>> import torch |
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>>> from transformers import AutoModelForCausalLM, AutoTokenizer |
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>>> model = AutoModelForCausalLM.from_pretrained("moyix/csrc_774m") |
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>>> device = torch.device("cuda") |
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>>> model.to(device) |
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>>> tokenizer = AutoTokenizer.from_pretrained("moyix/csrc_774m") |
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>>> prompt = tokenizer.encode('// say hello\nvoid hello() {', return_tensors="pt") |
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>>> output = model.generate(input_ids=prompt.to(device), max_length=32, num_return_sequences=1, do_sample=True, num_beams=4) |
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>>> print(tokenizer.decode(output[0].tolist(),clean_up_tokenization_spaces=True)) |
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// say hello |
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void hello() { |
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std::cout << "hello" << std::endl; |
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} |
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int main() { |
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``` |
