gpt-neo-125M-code-search-py

GPT-Code-Clippy-125M-Code-Search-Py

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Model Description

GPT-CC-125M-Code-Search is a GPT-Neo-125M model finetuned using causal language modeling on only the python language in the CodeSearchNet Challenge dataset. This model is specialized to autocomplete methods in the python language.

Training data

CodeSearchNet Challenge dataset.

Training procedure

The training script used to train this model can be found here.

./run_clm_flax.py \
    --output_dir $HOME/gpt-neo-125M-code-search-py \
    --model_name_or_path="EleutherAI/gpt-neo-125M" \
    --dataset_name code_search_net \
    --dataset_config_name="python" \
    --do_train --do_eval \
    --block_size="512" \
    --per_device_train_batch_size="32" \
    --per_device_eval_batch_size="64" \
    --preprocessing_num_workers="8" \
    --learning_rate="1.2e-4" \
    --num_train_epochs 20 \
    --warmup_steps 3000 \
    --adam_beta1="0.9" \
    --adam_beta2="0.95" \
    --weight_decay="0.1" \
    --overwrite_output_dir \
    --logging_steps="25" \
    --eval_steps="500" \
    --push_to_hub="False" \
    --report_to="all" \
    --dtype="bfloat16" \
    --skip_memory_metrics="True" \
    --save_steps="500" \
    --save_total_limit 10 \
    --report_to="wandb" \
    --run_name="gpt-neo-125M-code-search-py"

Intended Use and Limitations

The model is finetuned methods from the python language and is intended to autocomplete python methods given some prompt (method signature and docstring).

How to use

You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run:

from transformers import AutoModelForCausalLM, AutoTokenizer, FlaxAutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained("flax-community/gpt-neo-125M-code-clippy-code-search-py")

tokenizer = AutoTokenizer.from_pretrained("flax-community/gpt-neo-125M-code-clippy-code-search-py")

prompt = """def greet(name):
  '''A function to greet user. Given a user name it should say hello'''
""" 

input_ids = tokenizer(prompt, return_tensors='pt').input_ids.to(device)

start = input_ids.size(1)

out = model.generate(input_ids, do_sample=True, max_length=50, num_beams=2, 

                     early_stopping=True, eos_token_id=tokenizer.eos_token_id, )

print(tokenizer.decode(out[0][start:]))

Limitations and Biases

The model is intended to be used for research purposes and comes with no guarantees of quality of generated code.

GPT-CC is finetuned from GPT-Neo and might have inherited biases and limitations from it. See GPT-Neo model card for details.

Eval results

Coming soon...