Create README.md

README.md

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+---
+language: "multilingual"
+thumbnail: "https://doesnotexist.codes/messlab.png"
+tags:
+- programming
+- gpt2
+- causal-lm
+license: "Public Domain"
+
+---
+
+# 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](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.
+
+The processed dataset (in JSON format) can be found here: [csrc\_dataset\_large.json.gz](https://moyix.net/~moyix/csrc_dataset_large.json.gz).
+
+This model was used to generate snippets for the web site [This Code Does Not Exist](https://doesnotexist.codes/).
+
+# 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() {
+
+```