SantaCoder
Play with the model on the SantaCoder Space Demo.
Table of Contents
Model Summary
This is the Megatron-version of SantaCoder. We refer the reader to the SantaCoder model page for full documentation about this model
- Repository: bigcode/Megatron-LM
- Project Website: bigcode-project.org
- Paper: 🎅SantaCoder: Don't reach for the stars!🌟
- Point of Contact: contact@bigcode-project.org
- Languages: Python, Java, and JavaScript
Use
Intended use
The model was trained on GitHub code. As such it is not an instruction model and commands like "Write a function that computes the square root." do not work well.
You should phrase commands like they occur in source code such as comments (e.g. # the following function computes the sqrt) or write a function signature and docstring and let the model complete the function body.
Attribution & Other Requirements
The pretraining dataset of the model was filtered for permissive licenses only. Nevertheless, the model can generate source code verbatim from the dataset. The code's license might require attribution and/or other specific requirements that must be respected. We provide a search index that let's you search through the pretraining data to identify where generated code came from and apply the proper attribution to your code.
Limitations
The model has been trained on source code in Python, Java, and JavaScript. The predominant language in source is English although other languages are also present. As such the model is capable to generate code snippets provided some context but the generated code is not guaranteed to work as intended. It can be inefficient, contain bugs or exploits.
Training
Model
- Architecture: GPT-2 model with multi-query attention and Fill-in-the-Middle objective
- Pretraining steps: 600K
- Pretraining tokens: 236 billion
- Precision: float16
Hardware
- GPUs: 96 Tesla V100
- Training time: 6.2 days
- Total FLOPS: 2.1 x 10e21
Software
- Orchestration: Megatron-LM
- Neural networks: PyTorch
- FP16 if applicable: apex
License
The model is licenses under the CodeML Open RAIL-M v0.1 license. You can find the full license here.
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Dataset used to train bigcode/santacoder-megatron
Evaluation results
- pass@1 on MultiPL HumanEval (Python)self-reported0.180
- pass@10 on MultiPL HumanEval (Python)self-reported0.290
- pass@100 on MultiPL HumanEval (Python)self-reported0.490
- pass@1 on MultiPL MBPP (Python)self-reported0.350
- pass@10 on MultiPL MBPP (Python)self-reported0.580
- pass@100 on MultiPL MBPP (Python)self-reported0.770
- pass@1 on MultiPL HumanEval (JavaScript)self-reported0.160
- pass@10 on MultiPL HumanEval (JavaScript)self-reported0.270
- pass@100 on MultiPL HumanEval (JavaScript)self-reported0.470
- pass@1 on MultiPL MBPP (Javascript)self-reported0.280
- pass@10 on MultiPL MBPP (Javascript)self-reported0.510
- pass@100 on MultiPL MBPP (Javascript)self-reported0.700
- pass@1 on MultiPL HumanEval (Java)self-reported0.150
- pass@10 on MultiPL HumanEval (Java)self-reported0.260
- pass@100 on MultiPL HumanEval (Java)self-reported0.410
- pass@1 on MultiPL MBPP (Java)self-reported0.280
- pass@10 on MultiPL MBPP (Java)self-reported0.440
- pass@100 on MultiPL MBPP (Java)self-reported0.590
- single_line on HumanEval FIM (Python)self-reported0.440
- single_line on MultiPL HumanEval FIM (Java)self-reported0.620
- single_line on MultiPL HumanEval FIM (JavaScript)self-reported0.600
- BLEU on CodeXGLUE code-to-text (Python)self-reported18.130