pytorch gpt2 text-generation code dataset:codeparrot/github-code-clean dataset:openai_humaneval transformers generation license:apache-2.0
---
language: 
  - code
license: apache-2.0
tags:
- code
- gpt2
- generation
datasets:
- "codeparrot/github-code-clean"
- "openai_humaneval"
metrics:
- "evaluate-metric/code_eval"
---

# CodeParrot-Multi 🦜 (small)

CodeParrot-Multi 🦜 is a GPT-2 model (110M parameters) trained to generate code in 9 programming languages: "Java", "JavaScript", "PHP", "Python", "C#", "C++", "GO", "Ruby" and "TypeScript".

## Usage

You can load the CodeParrot-Multi model and tokenizer directly in `transformers`:

```Python
from transformers import AutoTokenizer, AutoModelWithLMHead
  
tokenizer = AutoTokenizer.from_pretrained("codeparrot/codeparrot-small-multi")
model = AutoModelWithLMHead.from_pretrained("codeparrot/codeparrot-small-multi")

inputs = tokenizer("def hello_world():", return_tensors="pt")
outputs = model(**inputs)
```

or with a `pipeline`:

```Python
from transformers import pipeline

pipe = pipeline("text-generation", model="codeparrot/codeparrot-small-multi")
outputs = pipe("def hello_world():")
```

## Training

The model was trained on the small [Github code small](https://huggingface.co/datasets/loubnabnl/github-small-near-dedup) after near deduplication, a subset of [Github code dataset](https://huggingface.co/datasets/codeparrot/github-code-clean) with the following settings:

|Config|Value|
|-------|-----|
|Batch size| 192 |
|Context size| 1024 |
|Training steps| 300'000|
|Gradient accumulation| 2|
|Gradient checkpointing| False|
|Learning rate| 5e-4 |
|Weight decay | 0.1 |
|Warmup steps| 2000 |
|Schedule| Cosine |

The training was executed on 16 x A100 (40GB) GPUs. This setting amounts to roughly 58 billion tokens.

## Performance

We evaluated the model on OpenAI's [HumanEval](https://huggingface.co/datasets/openai_humaneval) benchmark which consists of programming challenges:

| Metric | Value |
|-------|-----|
|pass@1 | --% |
|pass@10 | --%	 |
|pass@100 | --% |

The [pass@k metric](https://huggingface.co/metrics/code_eval) tells the probability that at least one out of k generations passes the tests. 

## Resources

- Code: [repository](https://github.com/huggingface/transformers/tree/master/examples/research_projects/codeparrot)
