Edit model card

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

Downloads last month
400
Safetensors
Model size
1.53B params
Tensor type
F32
·
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.

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