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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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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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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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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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scaled_model_params = model_params_count * 1e-6 |
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scale_suffix = "M" |
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else: |
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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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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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