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from dataclasses import dataclass, make_dataclass | |
from enum import Enum | |
import pandas as pd | |
from src.about import Tasks | |
def fields(raw_class): | |
return [v for k, v in raw_class.__dict__.items() if k[:2] != "__" and k[-2:] != "__"] | |
# These classes are for user facing column names, | |
# to avoid having to change them all around the code | |
# when a modif is needed | |
class ColumnContent: | |
name: str | |
type: str | |
displayed_by_default: bool | |
hidden: bool = False | |
never_hidden: bool = False | |
class AutoEvalColumn: | |
model_type_symbol = ColumnContent("model_type_symbol", "str", True, never_hidden=True) | |
model = ColumnContent("model", "markdown", True, never_hidden=True) | |
average = ColumnContent("average", "number", True) | |
anli = ColumnContent("ANLI", "number", True) | |
logiqa = ColumnContent("LogiQA", "number", True) | |
model_type = ColumnContent("model_type", "str", False) | |
architecture = ColumnContent("architecture", "str", False) | |
weight_type = ColumnContent("weight_type", "str", False, True) | |
precision = ColumnContent("precision", "str", False) | |
license = ColumnContent("license", "str", False) | |
params = ColumnContent("#Params (B)", "number", False) | |
likes = ColumnContent("Hub β€οΈ", "number", False) | |
still_on_hub = ColumnContent("Available on the hub", "bool", False) | |
revision = ColumnContent("Model sha", "str", False, False) | |
## For the queue columns in the submission tab | |
class EvalQueueColumn: # Queue column | |
model = ColumnContent("model", "markdown", True) | |
revision = ColumnContent("revision", "str", True) | |
private = ColumnContent("private", "bool", True) | |
precision = ColumnContent("precision", "str", True) | |
weight_type = ColumnContent("weight_type", "str", "Original") | |
status = ColumnContent("status", "str", True) | |
## All the model information that we might need | |
class ModelDetails: | |
name: str | |
display_name: str = "" | |
symbol: str = "" # emoji | |
class ModelType(Enum): | |
PT = ModelDetails(name="pretrained", symbol="π’") | |
FT = ModelDetails(name="fine-tuned", symbol="πΆ") | |
IFT = ModelDetails(name="instruction-tuned", symbol="β") | |
RL = ModelDetails(name="RL-tuned", symbol="π¦") | |
Unknown = ModelDetails(name="", symbol="?") | |
def to_str(self, separator=" "): | |
return f"{self.value.symbol}{separator}{self.value.name}" | |
def from_str(type): | |
if "fine-tuned" in type or "πΆ" in type: | |
return ModelType.FT | |
if "pretrained" in type or "π’" in type: | |
return ModelType.PT | |
if "RL-tuned" in type or "π¦" in type: | |
return ModelType.RL | |
if "instruction-tuned" in type or "β" in type: | |
return ModelType.IFT | |
return ModelType.Unknown | |
class WeightType(Enum): | |
Adapter = ModelDetails("Adapter") | |
Original = ModelDetails("Original") | |
Delta = ModelDetails("Delta") | |
class Precision(Enum): | |
float16 = ModelDetails("float16") | |
bfloat16 = ModelDetails("bfloat16") | |
float32 = ModelDetails("float32") | |
# qt_8bit = ModelDetails("8bit") | |
# qt_4bit = ModelDetails("4bit") | |
# qt_GPTQ = ModelDetails("GPTQ") | |
Unknown = ModelDetails("?") | |
def from_str(precision): | |
if precision in ["torch.float16", "float16"]: | |
return Precision.float16 | |
if precision in ["torch.bfloat16", "bfloat16"]: | |
return Precision.bfloat16 | |
if precision in ["float32"]: | |
return Precision.float32 | |
# if precision in ["8bit"]: | |
# return Precision.qt_8bit | |
# if precision in ["4bit"]: | |
# return Precision.qt_4bit | |
# if precision in ["GPTQ", "None"]: | |
# return Precision.qt_GPTQ | |
return Precision.Unknown | |
# Column selection | |
COLS = [c.name for c in fields(AutoEvalColumn) if not c.hidden] | |
EVAL_COLS = [c.name for c in fields(EvalQueueColumn)] | |
EVAL_TYPES = [c.type for c in fields(EvalQueueColumn)] | |
BENCHMARK_COLS = [t.value.col_name for t in Tasks] | |