File size: 3,107 Bytes
5d7514b | 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 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 | from __future__ import annotations
from typing import Literal
def fill_templated_filename(filename: str, output_type: str | None) -> str:
# Given a file name fill in any type templates e.g. 'some-model-name.{ftype}.gguf'
ftype_lowercase: str = output_type.lower() if output_type is not None else ""
ftype_uppercase: str = output_type.upper() if output_type is not None else ""
return filename.format(
ftype_lowercase,
outtype=ftype_lowercase,
ftype=ftype_lowercase,
OUTTYPE=ftype_uppercase,
FTYPE=ftype_uppercase,
)
def model_weight_count_rounded_notation(
model_params_count: int, min_digits: int = 2
) -> str:
if model_params_count > 1e12:
# Trillions Of Parameters
scaled_model_params = model_params_count * 1e-12
scale_suffix = "T"
elif model_params_count > 1e9:
# Billions Of Parameters
scaled_model_params = model_params_count * 1e-9
scale_suffix = "B"
elif model_params_count > 1e6:
# Millions Of Parameters
scaled_model_params = model_params_count * 1e-6
scale_suffix = "M"
else:
# Thousands Of Parameters
scaled_model_params = model_params_count * 1e-3
scale_suffix = "K"
fix = max(min_digits - len(str(round(scaled_model_params)).lstrip("0")), 0)
return f"{scaled_model_params:.{fix}f}{scale_suffix}"
def size_label(
total_params: int, shared_params: int, expert_params: int, expert_count: int
) -> str:
if expert_count > 0:
pretty_size = model_weight_count_rounded_notation(
abs(shared_params) + abs(expert_params), min_digits=2
)
size_class = f"{expert_count}x{pretty_size}"
else:
size_class = model_weight_count_rounded_notation(
abs(total_params), min_digits=2
)
return size_class
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:
# Reference: https://github.com/ggerganov/ggml/blob/master/docs/gguf.md#gguf-naming-convention
if base_name is not None:
name = base_name.strip().replace(" ", "-").replace("/", "-")
elif model_name is not None:
name = model_name.strip().replace(" ", "-").replace("/", "-")
else:
name = "ggml-model"
parameters = f"-{size_label}" if size_label is not None else ""
finetune = (
f"-{finetune_string.strip().replace(' ', '-')}"
if finetune_string is not None
else ""
)
version = (
f"-{version_string.strip().replace(' ', '-')}"
if version_string is not None
else ""
)
encoding = (
f"-{output_type.strip().replace(' ', '-').upper()}"
if output_type is not None
else ""
)
kind = f"-{model_type.strip().replace(' ', '-')}" if model_type is not None else ""
return f"{name}{parameters}{finetune}{version}{encoding}{kind}"
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