tiny_starcoder_py/README.md

pipeline_tag, inference, widget, license, datasets, metrics, library_name, tags, model-index

pipeline_tag	inference	widget	license	datasets	metrics	library_name	tags	model-index
text-generation	true	text example_title group def print_hello_world(): Hello world Python	bigcode-openrail-m	bigcode/the-stack-dedup	code_eval	transformers	code	name results Tiny-StarCoder-Py task dataset metrics type text-generation type name openai_humaneval HumanEval name type value verified pass@1 pass@1 7.84% false

TinyStarCoderPy

This is a 164M parameters model with the same architecture as StarCoder (8k context length, MQA & FIM). It was trained on the Python data from StarCoderData for ~6 epochs which amounts to 100B tokens.

Use

Intended use

The model was trained on GitHub code, to assist with some tasks like Assisted Generation. For pure code completion, we advise using our 15B models StarCoder or StarCoderBase.

Generation

# pip install -q transformers
from transformers import AutoModelForCausalLM, AutoTokenizer

checkpoint = "bigcode/tiny_starcoder_py"
device = "cuda" # for GPU usage or "cpu" for CPU usage

tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)

inputs = tokenizer.encode("def print_hello_world():", return_tensors="pt").to(device)
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))

Fill-in-the-middle

Fill-in-the-middle uses special tokens to identify the prefix/middle/suffix part of the input and output:

input_text = "<fim_prefix>def print_one_two_three():\n    print('one')\n    <fim_suffix>\n    print('three')<fim_middle>"
inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))

Training

Model

• Architecture: GPT-2 model with multi-query attention and Fill-in-the-Middle objective
• Pretraining steps: 50k
• Pretraining tokens: 100 billion
• Precision: bfloat16

Hardware

• GPUs: 32 Tesla A100
• Training time: 18 hours

Software

• Orchestration: Megatron-LM
• Neural networks: PyTorch
• BP16 if applicable: apex

License

The model is licensed under the BigCode OpenRAIL-M v1 license agreement. You can find the full agreement here.