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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+right_data_with_gold_shuffle.json filter=lfs diff=lfs merge=lfs -text

get_concept.py

@@ -0,0 +1,59 @@
+from transformers import AutoTokenizer
+from vllm import LLM, SamplingParams
+import argparse
+import json
+from tqdm import tqdm
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--start', type=int,help='模型路径')
+parser.add_argument('--end', type=int,help='模型路径')
+args = parser.parse_args()
+### 分片跑数据
+
+modelpath = "/dev_data/swzhang/model/LLM-Research/Meta-Llama-3-8B-Instruct/"
+
+# Initialize the tokenizer
+tokenizer = AutoTokenizer.from_pretrained(modelpath, trust_remote_code=True)
+
+# Input the model name or path. Can be GPTQ or AWQ models.
+llm = LLM(modelpath, dtype='float16', tensor_parallel_size=1, trust_remote_code=True, enforce_eager=True)
+sampling_params = SamplingParams(temperature=0.5, top_p=0.9, max_tokens=16000)
+num_concepts = 5
+system_prompt = "You are a pirate chatbot who always responds in pirate speak!"
+with open('right_data_with_gold_shuffle.json','r',encoding='utf-8')as f:
+    lines = f.readlines()[args.start:args.end]
+
+prompts = []
+for line in tqdm(lines):
+    one_data = json.loads(line)
+    problem = one_data['question']
+    prompt = f""" As an expert in educational assessment, analyze this problem:
+ {problem}
+ Break downandidentify {num_concepts} foundational concepts being tested. List these knowledge
+ points that:
+ • Are core curriculum concepts typically taught in standard courses,
+ • Are precise and measurable (not vague like "understanding math"),
+ • Are essential building blocks needed to solve this problem,
+ • Represent fundamental principles rather than problem-specific techniques.
+ Think through your analysis step by step, then format your response as a Python code snippet
+ containing a list of {num_concepts} strings, where each string clearly describes one fundamental
+ knowledge point."""
+    messages = [{"role": "system", "content": system_prompt}, {"role": "user", "content": prompt}]
+    one_prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True,tokenize=False)
+
+    prompts.append(one_prompt)
+
+outputs = llm.generate(prompts=prompts, sampling_params=sampling_params)
+
+concepts_data = []
+for i in range(len(outputs)):
+    one_data = json.loads(lines[i])
+    output = outputs[i].outputs[0].text
+    one_data['concepts_output'] = output
+    concepts_data.append(one_data)
+
+with open(f'get_concept_right_{args.start}_{args.end}.json','w',encoding='utf-8') as f:
+    for one in concepts_data:
+        f.write(json.dumps(one,ensure_ascii=False)+"\n")
+
+

right_data_with_gold_shuffle.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e530e86d7468ea786018615a1f5db951785f7c2c1e89fc567b1ae8504ca0f60d
+size 1666288201