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Add llamacpp to models.py
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@ -42,9 +42,10 @@ def load_model(model_name):
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t0 = time.time()
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shared.is_RWKV = 'rwkv-' in model_name.lower()
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shared.is_llamacpp = model_name.lower().startswith('llamacpp-')
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# Default settings
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if not any([shared.args.cpu, shared.args.load_in_8bit, shared.args.wbits, shared.args.auto_devices, shared.args.disk, shared.args.gpu_memory is not None, shared.args.cpu_memory is not None, shared.args.deepspeed, shared.args.flexgen, shared.is_RWKV]):
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if not any([shared.args.cpu, shared.args.load_in_8bit, shared.args.wbits, shared.args.auto_devices, shared.args.disk, shared.args.gpu_memory is not None, shared.args.cpu_memory is not None, shared.args.deepspeed, shared.args.flexgen, shared.is_RWKV, shared.is_llamacpp]):
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if any(size in shared.model_name.lower() for size in ('13b', '20b', '30b')):
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model = AutoModelForCausalLM.from_pretrained(Path(f"{shared.args.model_dir}/{shared.model_name}"), device_map='auto', load_in_8bit=True)
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else:
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@ -100,6 +101,12 @@ def load_model(model_name):
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model = load_quantized(model_name)
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# LLAMACPP model
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elif shared.is_llamacpp:
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from modules.llamacpp_model import LlamaCppModel
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model, tokenizer = LlamaCppModel.from_pretrained(Path(f'models/{model_name}/ggml-model-q4_0.bin'))
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return model, tokenizer
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# Custom
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else:
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params = {"low_cpu_mem_usage": True}
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