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Remove unnecessary shared.previous_model_name variable
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@ -13,8 +13,8 @@ global_scores = None
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def get_next_logits(*args, **kwargs):
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if shared.args.idle_timeout > 0 and shared.model is None and shared.previous_model_name not in [None, 'None']:
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shared.model, shared.tokenizer = load_model(shared.previous_model_name)
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if shared.args.idle_timeout > 0 and shared.model is None and shared.model_name not in [None, 'None']:
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shared.model, shared.tokenizer = load_model(shared.model_name)
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needs_lock = not args[2] # use_samplers
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if needs_lock:
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@ -370,7 +370,6 @@ def clear_torch_cache():
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def unload_model(keep_model_name=False):
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shared.model = shared.tokenizer = None
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shared.previous_model_name = shared.model_name
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shared.lora_names = []
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shared.model_dirty_from_training = False
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clear_torch_cache()
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@ -13,7 +13,6 @@ from modules.logging_colors import logger
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model = None
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tokenizer = None
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model_name = 'None'
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previous_model_name = 'None'
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is_seq2seq = False
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model_dirty_from_training = False
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lora_names = []
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@ -32,8 +32,8 @@ from modules.models import clear_torch_cache, load_model
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def generate_reply(*args, **kwargs):
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if shared.args.idle_timeout > 0 and shared.model is None and shared.previous_model_name not in [None, 'None']:
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shared.model, shared.tokenizer = load_model(shared.previous_model_name)
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if shared.args.idle_timeout > 0 and shared.model is None and shared.model_name not in [None, 'None']:
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shared.model, shared.tokenizer = load_model(shared.model_name)
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shared.generation_lock.acquire()
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try:
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