The SantaCoder models are a series of 1.1B parameter models trained on the Python, Java, and JavaScript subset of The Stack (v1.1) (which excluded opt-out requests).
The main model uses Multi Query Attention, a context window of 2048 tokens, and was trained using near-deduplication and comment-to-code ratio as filtering criteria and using the Fill-in-the-Middle objective.
In addition there are several models that were trained on datasets with different filter parameters and with architecture and objective variations.
Stronger near-deduplication and comment-to-code ratio
The final model is the best performing model and was trained twice as long (236B tokens) as the others. This checkpoint is the default model and available on the main branch. All other checkpoints are on separate branches with according names.
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.
Feel free to share your generations in the Community tab!
How to use
Generation
python
1# pip install -q transformers2from transformers import AutoModelForCausalLM, AutoTokenizer
34checkpoint ="bigcode/santacoder"5device ="cuda"# for GPU usage or "cpu" for CPU usage67tokenizer = AutoTokenizer.from_pretrained(checkpoint)8model = AutoModelForCausalLM.from_pretrained(checkpoint, trust_remote_code=True).to(device)910inputs = tokenizer.encode("def print_hello_world():", return_tensors="pt").to(device)11outputs = model.generate(inputs)12print(tokenizer.decode(outputs[0]))
Fill-in-the-middle
Fill-in-the-middle uses special tokens to identify the prefix/middle/suffix part of the input and output:
Make sure to use <fim-prefix>, <fim-suffix>, <fim-middle> and not <fim_prefix>, <fim_suffix>, <fim_middle> as in StarCoder models.
Load other checkpoints
We upload the checkpoint of each experiment to a separate branch as well as the intermediate checkpoints as commits on the branches. You can load them with the revision flag:
python
1model = AutoModelForCausalLM.from_pretrained(2"bigcode/santacoder",3 revision="no-fim",# name of branch or commit hash4 trust_remote_code=True5)
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
The model is licensed under the BigCode OpenRAIL-M v1 license agreement. You can find the full agreement here.
Citation
@article{allal2023santacoder,
title={SantaCoder: don't reach for the stars!},
author={Allal, Loubna Ben and Li, Raymond and Kocetkov, Denis and Mou, Chenghao and Akiki, Christopher and Ferrandis, Carlos Munoz and Muennighoff, Niklas and Mishra, Mayank and Gu, Alex and Dey, Manan and others},
journal={arXiv preprint arXiv:2301.03988},
year={2023}
}
