Create README.md

README.md

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+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "wangrongsheng/DPDG-Qwen2-7B-lora" # [wangrongsheng/DPDG-Qwen2-7B-lora]
+device = "cuda" # the device to load the model onto
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+ins = """
+请根据给定的提示生成两种不同质量的回答。第一种回答应该是高质量的、令人满意的答案，代表"chosen"的选项。第二种回答则应该是低质量的、不太理想的答案，代表"rejected"的选项。\n
+在生成这两个回答时，请注意以下事项：\n
+1. "chosen" 回复应具有实质性内容、流畅的表达，并能够完整回答提示中提出的问题或要求。\n
+2. "rejected" 回复可能存在一些问题，例如逻辑不连贯、信息不完整或表达不清晰。但请确保它仍然是一个可以大致理解的回复，而不是完全无关或毫无意义的内容。\n
+3. 这两个回复的长度应该大致相当，而不是差异极大。\n
+4. 请确保在"chosen"回复和"rejected"回复之间反映出明显的质量差异，使区别显而易见。\n
+请根据这些指导方针为给定的提示生成一个"chosen"的回应和一个"rejected"的回应。这将有助于训练奖励模型以区分高质量和低质量的回应。\n
+提示是:
+"""
+prompt = "什么是ACI fabric中的叶脊拓扑结构？"
+messages = [
+    {"role": "system", "content": ins},
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(device)
+
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=1024
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+
+print(response)
+```