Total runs:
226.4K
Run Growth:
-38.9K
Growth Rate:
-17.20%
Sponsored by
SocQ
bigcode / santacoder-megatron
Model's Last Updated: January 05 2023
text-generation
Introduction of santacoder-megatron
Model Details of santacoder-megatron
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: [email protected]
- 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 .
Runs of bigcode santacoder-megatron on huggingface.co
0
Total runs
0
24-hour runs
0
3-day runs
0
7-day runs
0
30-day runs
More Information About santacoder-megatron huggingface.co Model
Url of santacoder-megatron
santacoder-megatron huggingface.co Url
Provider of santacoder-megatron huggingface.co
Other API from bigcode
Updated:June 01 2023
Total runs:
165.5K
Run Growth:
-20.6K
Growth Rate:
-12.42%
Total runs:
32.9K
Run Growth:
-12.5K
Growth Rate:
-37.94%
Total runs:
1.6K
Run Growth:
-2.3K
Growth Rate:
-136.24%
Total runs:
562
Run Growth:
-507
Growth Rate:
-84.92%
Total runs:
295
Run Growth:
292
Growth Rate:
98.98%
Total runs:
202
Run Growth:
22
Growth Rate:
11.22%
Total runs:
189
Run Growth:
-9
Growth Rate:
-4.74%
Total runs:
134
Run Growth:
-282
Growth Rate:
-207.35%
Total runs:
98
Run Growth:
63
Growth Rate:
64.95%
Total runs:
27
Run Growth:
19
Growth Rate:
76.00%
Total runs:
3
Run Growth:
1
Growth Rate:
33.33%
Total runs:
2
Run Growth:
-3
Growth Rate:
-150.00%
Total runs:
2
Run Growth:
-3
Growth Rate:
-150.00%
Total runs:
2
Run Growth:
1
Growth Rate:
50.00%
Total runs:
1
Run Growth:
-2
Growth Rate:
-200.00%
Total runs:
0
Run Growth:
0
Growth Rate:
0.00%
Total runs:
0
Run Growth:
0
Growth Rate:
0.00%
Total runs:
0
Run Growth:
0
Growth Rate:
0.00%
Total runs:
0
Run Growth:
0
Growth Rate:
0.00%
Total runs:
0
Run Growth:
0
Growth Rate:
0.00%