add Demo usage

README.md

@@ -9,7 +9,6 @@ metrics:
 library_name: transformers
 pipeline_tag: text-generation
 ---
-
 <p align="center"><h2 align="center">Unleashing Reasoning Capability of LLMs via Scalable Question Synthesis from Scratch</h2></p>
 
 # Model Card for Qwen2-Math-7B-ScaleQuest 
@@ -46,6 +45,38 @@ We release two question generator models and four problem-solving models.
 
 Below is an example using `Qwen2-Math-7B-ScaleQuest`
 ```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "dyyyyyyyy/Qwen2-Math-7B-ScaleQuest"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+question = "Find the value of $x$ that satisfies the equation $4x+5 = 6x+7$."
+
+sys_prompt="<|im_start|>system\nPlease reason step by step, and put your final answer within \\boxed{{}}.<|im_end|>\n"
+query_prompt="<|im_start|>user" + "\n"
+# {query}
+prompt_after_query="<|im_end|>" + "\n"
+resp_prompt="<|im_start|>assistant" + "\n"
+prompt_before_resp=""
+# {resp}
+delim="<|im_end|>" + "\n"
+
+prefix_prompt = f"{query_prompt}{question}{prompt_after_query}{resp_prompt}{prompt_before_resp}".rstrip(" ")
+full_prompt = sys_prompt + delim.join([prefix_prompt])
+
+# print(full_prompt)
+
+inputs = tokenizer(full_prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=512, do_sample=False)
+print(tokenizer.decode(outputs[0][len(inputs.input_ids[0]):], skip_special_tokens=True))
+
 ```
 
 ## Citation