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Code Generation using GPT2-Large

This is a GPT2-large model that's further fine-tuned on the Codeparrot dataset with a custom metric focused on code generation.
The Tokenizer is initialized from the GPT2-large and further trained on the same dataset to better align the tokenization for generating code.

Model description

This Model has the same architecture and Parameters as the GPT2-large model. Please refer to this link to know more about the model details.

Intended Use & Limitations

This model is intended to generate code for the required function based on a small description of the output required.

Note: The model is primarily trained with an objective of code generation.

Usage

You can use this model directly to get the summaries:

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load Code Generator LLM and tokenizer from checkpoint
tokenizer = AutoTokenizer.from_pretrained("DeathReaper0965/gpt2_large_code_generator")
model = AutoModelForCausalLM.from_pretrained("DeathReaper0965/gpt2_large_code_generator")
model = model.to("cuda" if torch.cuda.is_available() else "cpu")

inputs = tokenizer("def hello_world():", return_tensors="pt").to("cuda" if torch.cuda.is_available() else "cpu")

outputs = model.generate(**inputs,
                         max_new_tokens= 30,
                         num_return_sequences= 1)

print(tokenizer.batch_decode(outputs)[0])

###########OUTPUT###########
def hello_world():
    return "Hello World!"

@app.route("/hello_world")
def hello_world():
    return "Hello World!"

Designed and Developed with by Praneet | LinkedIn | GitHub

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Dataset used to train DeathReaper0965/gpt2-large-code-generator