README
CodeParrot 🦜 (small)
CodeParrot 🦜 is a GPT-2 model (110M parameters) trained to generate Python code.
Usage
You can load the CodeParrot model and tokenizer directly in transformers:
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("codeparrot/codeparrot-small")
model = AutoModelWithLMHead.from_pretrained("codeparrot/codeparrot-small")
inputs = tokenizer("def hello_world():", return_tensors="pt")
outputs = model(**inputs)
or with a pipeline:
from transformers import pipeline
pipe = pipeline("text-generation", model="codeparrot/codeparrot-small")
outputs = pipe("def hello_world():")
Training
The model was trained on the cleaned CodeParrot 🦜 dataset with the following settings:
| Config | Value |
|---|---|
| Batch size | 192 |
| Context size | 1024 |
| Training steps | 150'000 |
| Gradient accumulation | 1 |
| 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 29 billion tokens.
Performance
We evaluated the model on OpenAI's HumanEval benchmark which consists of programming challenges:
| Metric | Value |
|---|---|
| pass@1 | 3.80% |
| pass@10 | 6.57% |
| pass@100 | 12.78% |
The pass@k metric tells the probability that at least one out of k generations passes the tests.
Resources
- Dataset: full, train, valid
- Code: repository
- Spaces: generation, highlighting
Similar repos
Used in code
README
CodeParrot 🦜 (small)
CodeParrot 🦜 is a GPT-2 model (110M parameters) trained to generate Python code.
Usage
You can load the CodeParrot model and tokenizer directly in transformers:
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("codeparrot/codeparrot-small")
model = AutoModelWithLMHead.from_pretrained("codeparrot/codeparrot-small")
inputs = tokenizer("def hello_world():", return_tensors="pt")
outputs = model(**inputs)
or with a pipeline:
from transformers import pipeline
pipe = pipeline("text-generation", model="codeparrot/codeparrot-small")
outputs = pipe("def hello_world():")
Training
The model was trained on the cleaned CodeParrot 🦜 dataset with the following settings:
| Config | Value |
|---|---|
| Batch size | 192 |
| Context size | 1024 |
| Training steps | 150'000 |
| Gradient accumulation | 1 |
| 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 29 billion tokens.
Performance
We evaluated the model on OpenAI's HumanEval benchmark which consists of programming challenges:
| Metric | Value |
|---|---|
| pass@1 | 3.80% |
| pass@10 | 6.57% |
| pass@100 | 12.78% |
The pass@k metric tells the probability that at least one out of k generations passes the tests.
Resources
- Dataset: full, train, valid
- Code: repository
- Spaces: generation, highlighting