File size: 1,936 Bytes
61e43e6 | 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 | """Transformers-compatible Multiscreen implementation.
This package ports the core architecture from ``dieOD/multiscreen-pytorch`` to
Hugging Face Transformers-style ``PreTrainedConfig`` / ``PreTrainedModel``
classes.
"""
from .configuration_multiscreen import MultiscreenConfig
from .compile_utils import find_msvc_cl, load_vcvars_env, setup_compile_env
from .data import PackedTextDataset
from .modeling_multiscreen import (
GatedScreeningBlock,
MultiscreenForCausalLM,
MultiscreenLayer,
MultiscreenModel,
MultiscreenPreTrainedModel,
ScreeningCache,
convert_original_state_dict_for_causal_lm,
convert_original_state_dict_for_model,
)
__version__ = "0.1.2"
__all__ = [
"MultiscreenConfig",
"MultiscreenPreTrainedModel",
"MultiscreenModel",
"MultiscreenForCausalLM",
"MultiscreenLayer",
"GatedScreeningBlock",
"ScreeningCache",
"convert_original_state_dict_for_causal_lm",
"convert_original_state_dict_for_model",
"PackedTextDataset",
"find_msvc_cl",
"load_vcvars_env",
"setup_compile_env",
"register_multiscreen_auto_classes",
]
def register_multiscreen_auto_classes() -> None:
"""Register Multiscreen with Transformers auto classes in this process.
Use this when loading local checkpoints without ``trust_remote_code`` and
without installing the model into a Transformers source tree::
from multiscreen_transformers import register_multiscreen_auto_classes
register_multiscreen_auto_classes()
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("./checkpoint")
"""
from transformers import AutoConfig, AutoModel, AutoModelForCausalLM
AutoConfig.register(MultiscreenConfig.model_type, MultiscreenConfig)
AutoModel.register(MultiscreenConfig, MultiscreenModel)
AutoModelForCausalLM.register(MultiscreenConfig, MultiscreenForCausalLM)
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