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| from modules import shared | |
| from modules.logging_colors import logger | |
| from modules.LoRA import add_lora_to_model | |
| from modules.models import load_model, unload_model | |
| from modules.models_settings import get_model_metadata, update_model_parameters | |
| from modules.utils import get_available_loras, get_available_models | |
| def get_current_model_info(): | |
| return { | |
| 'model_name': shared.model_name, | |
| 'lora_names': shared.lora_names, | |
| 'loader': shared.args.loader | |
| } | |
| def list_models(): | |
| return {'model_names': get_available_models()[1:]} | |
| def list_dummy_models(): | |
| result = { | |
| "object": "list", | |
| "data": [] | |
| } | |
| # these are expected by so much, so include some here as a dummy | |
| for model in ['gpt-3.5-turbo', 'text-embedding-ada-002']: | |
| result["data"].append(model_info_dict(model)) | |
| return result | |
| def model_info_dict(model_name: str) -> dict: | |
| return { | |
| "id": model_name, | |
| "object": "model", | |
| "created": 0, | |
| "owned_by": "user" | |
| } | |
| def _load_model(data): | |
| model_name = data["model_name"] | |
| args = data["args"] | |
| settings = data["settings"] | |
| unload_model() | |
| model_settings = get_model_metadata(model_name) | |
| update_model_parameters(model_settings) | |
| # Update shared.args with custom model loading settings | |
| if args: | |
| for k in args: | |
| if hasattr(shared.args, k): | |
| setattr(shared.args, k, args[k]) | |
| shared.model, shared.tokenizer = load_model(model_name) | |
| # Update shared.settings with custom generation defaults | |
| if settings: | |
| for k in settings: | |
| if k in shared.settings: | |
| shared.settings[k] = settings[k] | |
| if k == 'truncation_length': | |
| logger.info(f"TRUNCATION LENGTH (UPDATED): {shared.settings['truncation_length']}") | |
| elif k == 'instruction_template': | |
| logger.info(f"INSTRUCTION TEMPLATE (UPDATED): {shared.settings['instruction_template']}") | |
| def list_loras(): | |
| return {'lora_names': get_available_loras()[1:]} | |
| def load_loras(lora_names): | |
| add_lora_to_model(lora_names) | |
| def unload_all_loras(): | |
| add_lora_to_model([]) | |