2023-09-11 23:49:30 +02:00
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from modules import shared
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2023-11-08 05:58:06 +01:00
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from modules.models import load_model, unload_model
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from modules.models_settings import get_model_metadata, update_model_parameters
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2023-09-11 23:49:30 +02:00
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from modules.utils import get_available_models
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2023-07-11 23:50:08 +02:00
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2023-07-12 20:33:25 +02:00
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2023-11-08 03:59:02 +01:00
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def get_current_model_info():
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return {
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'model_name': shared.model_name,
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'lora_names': shared.lora_names
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}
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2023-11-08 04:59:27 +01:00
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def list_models():
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result = {
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2023-07-11 23:50:08 +02:00
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"object": "list",
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2023-11-08 04:59:27 +01:00
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"data": []
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2023-07-11 23:50:08 +02:00
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}
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2023-11-08 04:59:27 +01:00
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for model in get_dummy_models() + get_available_models()[1:]:
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result["data"].append(model_info_dict(model))
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2023-07-11 23:50:08 +02:00
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2023-11-08 04:59:27 +01:00
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return result
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2023-07-11 23:50:08 +02:00
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2023-11-08 04:59:27 +01:00
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def model_info_dict(model_name: str) -> dict:
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2023-07-11 23:50:08 +02:00
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return {
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"id": model_name,
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"object": "model",
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2023-11-08 04:59:27 +01:00
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"created": 0,
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"owned_by": "user"
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2023-07-11 23:50:08 +02:00
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}
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2023-11-08 04:59:27 +01:00
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def get_dummy_models() -> list:
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return [ # these are expected by so much, so include some here as a dummy
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'gpt-3.5-turbo',
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'text-embedding-ada-002',
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]
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2023-11-08 05:58:06 +01:00
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def _load_model(data):
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model_name = data["model_name"]
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args = data["args"]
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settings = data["settings"]
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unload_model()
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model_settings = get_model_metadata(model_name)
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update_model_parameters(model_settings, initial=True)
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# Update shared.args with custom model loading settings
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if args:
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for k in args:
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2023-11-08 06:34:13 +01:00
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if hasattr(shared.args, k):
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2023-11-08 05:58:06 +01:00
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setattr(shared.args, k, args[k])
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shared.model, shared.tokenizer = load_model(model_name)
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# Update shared.settings with custom generation defaults
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if settings:
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for k in settings:
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if k in shared.settings:
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shared.settings[k] = settings[k]
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