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import torch |
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from peft import PeftModel |
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from transformers import AutoModelForCausalLM, AutoTokenizer, LlamaTokenizer, StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer |
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model_name = "./llama2-hf" |
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adapters_name = './checkpoint-760/adapter_model' |
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print(f"Starting to load the model {model_name} into memory") |
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m = AutoModelForCausalLM.from_pretrained( |
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model_name, |
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torch_dtype=torch.bfloat16, |
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) |
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m = PeftModel.from_pretrained(m, adapters_name) |
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m = m.merge_and_unload() |
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print(f"Successfully loaded the model {model_name} into memory") |
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m.save_pretrained("nyc-savvy") |
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