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([])