VityaVitalich
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Update README.md
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README.md
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@@ -72,9 +72,9 @@ config = PeftConfig.from_pretrained('VityaVitalich/TaxoLLaMA')
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# Do not forget your token for Llama2 models
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model = LlamaForCausalLM.from_pretrained(config.base_model_name_or_path, load_in_4bit=True, torch_dtype=torch.bfloat16)
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tokenizer = LlamaTokenizer.from_pretrained(config.base_model_name_or_path)
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inference_model = PeftModel.from_pretrained(model, 'VityaVitalich/TaxoLLaMA'
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processed_term =
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system_prompt = """<s>[INST] <<SYS>> You are a helpfull assistant. List all the possible words divided with a coma. Your answer should not include anything except the words divided by a coma<</SYS>>"""
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processed_term = system_prompt + '\n' + processed_term + '[/INST]'
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# Do not forget your token for Llama2 models
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model = LlamaForCausalLM.from_pretrained(config.base_model_name_or_path, load_in_4bit=True, torch_dtype=torch.bfloat16)
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tokenizer = LlamaTokenizer.from_pretrained(config.base_model_name_or_path)
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inference_model = PeftModel.from_pretrained(model, 'VityaVitalich/TaxoLLaMA')
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processed_term = "hyponym: tiger | hypernyms:"
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system_prompt = """<s>[INST] <<SYS>> You are a helpfull assistant. List all the possible words divided with a coma. Your answer should not include anything except the words divided by a coma<</SYS>>"""
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processed_term = system_prompt + '\n' + processed_term + '[/INST]'
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