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---
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license: apache-2.0
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---
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---
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language:
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- code
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license: apache-2.0
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tags:
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- code
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- gpt2
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- generation
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datasets:
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- "codeparrot/github-code-clean"
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- "openai_humaneval"
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metrics:
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- "evaluate-metric/code_eval"
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---
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# CodeParrot-Multi 🦜 (small)
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CodeParrot-Multi 🦜 is a GPT-2 model (110M parameters) trained to generate code in 32 programming languages (Python, Java, C, JavaScript...)
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## Usage
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You can load the CodeParrot-Multi model and tokenizer directly in `transformers`:
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```Python
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from transformers import AutoTokenizer, AutoModelWithLMHead
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tokenizer = AutoTokenizer.from_pretrained("codeparrot/codeparrot-small-multi")
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model = AutoModelWithLMHead.from_pretrained("codeparrot/codeparrot-small-multi")
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inputs = tokenizer("def hello_world():", return_tensors="pt")
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outputs = model(**inputs)
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```
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or with a `pipeline`:
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```Python
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from transformers import pipeline
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pipe = pipeline("text-generation", model="codeparrot/codeparrot-small-multi")
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outputs = pipe("def hello_world():")
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```
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## Training
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The model was trained on the cleaned [Github code dataset](https://huggingface.co/datasets/codeparrot/github-code-clean) with the following settings:
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|Config|Value|
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|-------|-----|
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|Batch size| 192 |
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|Context size| 1024 |
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|Training steps| 300'000|
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|Gradient accumulation| 2|
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|Gradient checkpointing| False|
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|Learning rate| 5e-4 |
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|Weight decay | 0.1 |
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|Warmup steps| 2000 |
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|Schedule| Cosine |
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The training was executed on 16 x A100 (40GB) GPUs. This setting amounts to roughly 58 billion tokens.
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## Performance
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We evaluated the model on OpenAI's [HumanEval](https://huggingface.co/datasets/openai_humaneval) benchmark which consists of programming challenges:
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| Metric | Value |
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|-------|-----|
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|pass@1 | --% |
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|pass@10 | --% |
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|pass@100 | --% |
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The [pass@k metric](https://huggingface.co/metrics/code_eval) tells the probability that at least one out of k generations passes the tests.
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## Resources
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- Code: [repository](https://github.com/huggingface/transformers/tree/master/examples/research_projects/codeparrot)
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