Instructions to use jonl521/modeling_simple with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jonl521/modeling_simple with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jonl521/modeling_simple", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,042 Bytes
2216aae | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | # mypy: allow-untyped-defs
from torch._C import _compile_graph_to_code_table, _generate_upgraders_graph
def format_bytecode(table):
# given a nested tuple, convert it to nested list
def listify(content):
if not isinstance(content, tuple):
return content
return [listify(i) for i in content]
formatted_table = {}
for entry in table:
identifier = entry[0]
content = entry[1]
content = listify(content)
formatted_table[identifier] = content
return formatted_table
def generate_upgraders_bytecode() -> list:
yaml_content = []
upgraders_graph_map = _generate_upgraders_graph()
for upgrader_name, upgrader_graph in upgraders_graph_map.items():
bytecode_table = _compile_graph_to_code_table(upgrader_name, upgrader_graph)
entry = {upgrader_name: format_bytecode(bytecode_table)}
yaml_content.append(entry)
return yaml_content
if __name__ == "__main__":
raise RuntimeError("This file is not meant to be run directly")
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