add literature data

We tried to use the GPT3.5 API to integrate the conclusion in the medical literature as external information into multiple rounds of dialogue, and based on this, we fine-tuned the instructions of LLaMA.

infer_literature.py

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+import sys
+import json
+
+import fire
+import gradio as gr
+import torch
+import transformers
+from peft import PeftModel
+from transformers import GenerationConfig, LlamaForCausalLM, LlamaTokenizer
+
+from utils.prompter import Prompter
+
+if torch.cuda.is_available():
+    device = "cuda"
+
+def load_instruction(instruct_dir):
+    input_data = []
+    with open(instruct_dir, "r") as f:
+        lines = f.readlines()
+        for line in lines:
+            line = line.strip()
+            d = json.loads(line)
+            input_data.append(d)
+    return input_data
+
+
+def main(
+    load_8bit: bool = False,
+    base_model: str = "",
+    # the infer data, if not exists, infer the default instructions in code
+    single_or_multi: str = "",
+    use_lora: bool = True,
+    lora_weights: str = "tloen/alpaca-lora-7b",
+    # The prompt template to use, will default to med_template.
+    prompt_template: str = "med_template",
+):
+    prompter = Prompter(prompt_template)
+    tokenizer = LlamaTokenizer.from_pretrained(base_model)
+    model = LlamaForCausalLM.from_pretrained(
+        base_model,
+        load_in_8bit=load_8bit,
+        torch_dtype=torch.float16,
+        device_map="auto",
+    )
+    if use_lora:
+        print(f"using lora {lora_weights}")
+        model = PeftModel.from_pretrained(
+            model,
+            lora_weights,
+            torch_dtype=torch.float16,
+        )
+    # unwind broken decapoda-research config
+    model.config.pad_token_id = tokenizer.pad_token_id = 0  # unk
+    model.config.bos_token_id = 1
+    model.config.eos_token_id = 2
+    if not load_8bit:
+        model.half()  # seems to fix bugs for some users.
+
+    model.eval()
+
+    if torch.__version__ >= "2" and sys.platform != "win32":
+        model = torch.compile(model)
+
+    def evaluate(
+        instruction,
+        input=None,
+        temperature=0.1,
+        top_p=0.75,
+        top_k=40,
+        num_beams=4,
+        max_new_tokens=256,
+        **kwargs,
+    ):
+        prompt = prompter.generate_prompt(instruction, input)
+        inputs = tokenizer(prompt, return_tensors="pt")
+        input_ids = inputs["input_ids"].to(device)
+        generation_config = GenerationConfig(
+            temperature=temperature,
+            top_p=top_p,
+            top_k=top_k,
+            num_beams=num_beams,
+            **kwargs,
+        )
+        with torch.no_grad():
+            generation_output = model.generate(
+                input_ids=input_ids,
+                generation_config=generation_config,
+                return_dict_in_generate=True,
+                output_scores=True,
+                max_new_tokens=max_new_tokens,
+            )
+        s = generation_output.sequences[0]
+        output = tokenizer.decode(s)
+        return prompter.get_response(output)
+
+    if single_or_multi == "multi":
+
+        response=""
+        instruction=""
+        for _ in range(0,5):  
+            inp=input("请输入:")
+            inp="<user>: " + inp
+            instruction=instruction+inp
+            response=evaluate(instruction)
+            response=response.replace('\n','')
+            print("Response:", response)
+            instruction= instruction + " <bot>: " + response
+
+
+    elif single_or_multi == "single":
+        for instruction in [
+            "肝癌是什么？有哪些症状和迹象？",
+            "肝癌是如何诊断的？有哪些检查和测试可以帮助诊断？",
+            "Sorafenib是一种口服的多靶点酪氨酸激酶抑制剂，它的作用机制是什么？",
+            "Regorafenib是一种口服的多靶点酪氨酸激酶抑制剂，它的作用机制是什么？它和Sorafenib有什么不同？",
+            "肝癌药物治疗的副作用有哪些？如何缓解这些副作用？",
+            "肝癌药物治疗的费用高昂，如何降低治疗的经济负担？",
+            "我想了解一下β-谷甾醇是否可作为肝癌的治疗药物",
+            "能介绍一下最近Hsa＿circ＿0008583在肝细胞癌治疗中的潜在应用的研究么?"
+        ]:  
+            print("instruction:",instruction)
+            instruction="<user>: "+instruction
+            print("Response:", evaluate(instruction))
+          
+
+
+if __name__ == "__main__":
+    fire.Fire(main)