1932a1b871
Some models use acronyms in lowercase, which can't be title-cased like other words, so it's best to simply use the same case as in the original model name. Note that the size label still has an uppercased suffix to make it distinguishable from the context size of a finetune.
69 lines
2.9 KiB
Python
69 lines
2.9 KiB
Python
from __future__ import annotations
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from typing import Literal
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def fill_templated_filename(filename: str, output_type: str | None) -> str:
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# Given a file name fill in any type templates e.g. 'some-model-name.{ftype}.gguf'
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ftype_lowercase: str = output_type.lower() if output_type is not None else ""
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ftype_uppercase: str = output_type.upper() if output_type is not None else ""
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return filename.format(ftype_lowercase,
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outtype=ftype_lowercase, ftype=ftype_lowercase,
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OUTTYPE=ftype_uppercase, FTYPE=ftype_uppercase)
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def model_weight_count_rounded_notation(model_params_count: int, min_digits: int = 2) -> str:
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if model_params_count > 1e12 :
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# Trillions Of Parameters
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scaled_model_params = model_params_count * 1e-12
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scale_suffix = "T"
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elif model_params_count > 1e9 :
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# Billions Of Parameters
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scaled_model_params = model_params_count * 1e-9
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scale_suffix = "B"
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elif model_params_count > 1e6 :
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# Millions Of Parameters
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scaled_model_params = model_params_count * 1e-6
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scale_suffix = "M"
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else:
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# Thousands Of Parameters
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scaled_model_params = model_params_count * 1e-3
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scale_suffix = "K"
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fix = max(min_digits - len(str(round(scaled_model_params)).lstrip('0')), 0)
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return f"{scaled_model_params:.{fix}f}{scale_suffix}"
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def size_label(total_params: int, shared_params: int, expert_params: int, expert_count: int) -> str:
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if expert_count > 0:
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pretty_size = model_weight_count_rounded_notation(abs(shared_params) + abs(expert_params), min_digits=2)
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size_class = f"{expert_count}x{pretty_size}"
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else:
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size_class = model_weight_count_rounded_notation(abs(total_params), min_digits=2)
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return size_class
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def naming_convention(model_name: str | None, base_name: str | None, finetune_string: str | None, version_string: str | None, size_label: str | None, output_type: str | None, model_type: Literal['vocab', 'LoRA'] | None = None) -> str:
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# Reference: https://github.com/ggerganov/ggml/blob/master/docs/gguf.md#gguf-naming-convention
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if base_name is not None:
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name = base_name.strip().replace(' ', '-').replace('/', '-')
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elif model_name is not None:
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name = model_name.strip().replace(' ', '-').replace('/', '-')
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else:
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name = "ggml-model"
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parameters = f"-{size_label}" if size_label is not None else ""
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finetune = f"-{finetune_string.strip().replace(' ', '-')}" if finetune_string is not None else ""
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version = f"-{version_string.strip().replace(' ', '-')}" if version_string is not None else ""
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encoding = f"-{output_type.strip().replace(' ', '-').upper()}" if output_type is not None else ""
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kind = f"-{model_type.strip().replace(' ', '-')}" if model_type is not None else ""
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return f"{name}{parameters}{finetune}{version}{encoding}{kind}"
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