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26 lines
885 B
Python
26 lines
885 B
Python
import llamahf
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import os
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from accelerate import infer_auto_device_map
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# # to save memory use bfloat16
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# import torch
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# torch.set_default_dtype(torch.bfloat16)
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MODEL = 'decapoda-research/llama-7b-hf'
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# MODEL = 'decapoda-research/llama-13b-hf'
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# MODEL = 'decapoda-research/llama-30b-hf'
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# MODEL = 'decapoda-research/llama-65b-hf'
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if os.path.exists('./trained'):
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MODEL = './trained'
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tokenizer = llamahf.LLaMATokenizer.from_pretrained(MODEL)
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model = llamahf.LLaMAForCausalLM.from_pretrained(MODEL, low_cpu_mem_usage=True, device_map="auto", offload_folder="./offload")
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# will use 6 Gb of GPU VRAM, others to CPU RAM
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device_map = infer_auto_device_map(model, max_memory={0: "6GiB", "cpu": "128GiB"})
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print(device_map)
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batch = tokenizer("The highest mountain in China is ", return_tensors="pt")
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print(tokenizer.decode(model.generate(batch["input_ids"].cuda(), max_length=100)[0]))
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