Upload QWQ_infer.py with huggingface_hub

QWQ_infer.py

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+
+
+import json
+import pprint 
+
+
+def read_json(file_path): 
+    with open(file_path, 'r', encoding='utf-8') as file:
+        data = json.load(file)
+    return data
+
+def write_json(file_path, data):
+    with open(file_path, 'w', encoding='utf-8') as file:
+        json.dump(data, file, ensure_ascii=False, indent=4)
+        
+data = read_json("DataSet/train_samples_all_tuning.json")       
+        
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "Model/QwQ-32B-Preview"
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+
+
+def chat_QwQ(prompt):
+    
+    messages = [
+        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
+        {"role": "user", "content": prompt}
+    ]
+    
+    text = tokenizer.apply_chat_template(
+        messages,
+        tokenize=False,
+        add_generation_prompt=True
+    )
+    
+    model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+    generated_ids = model.generate(
+        **model_inputs,
+        max_new_tokens=512
+    )
+    
+    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)
+    return response
+
+
+
+
+# from transformers import MarianMTModel, MarianTokenizer
+
+# model_name = "Model/opus-mt-en-zh"
+# tokenizer = MarianTokenizer.from_pretrained(model_name)
+# model = MarianMTModel.from_pretrained(model_name)
+
+
+
+    
+
+for i in data:
+    sent1 = i['conversations'][0]['value']
+    sent2 = i['conversations'][1]['value']
+    sentence = sent1 + sent2 
+    prompt = "This is a question-answering datapoint based on image information. Determine whether the answer can be judged without relying on the image. If it can, this is considered bad data; if it requires the image, it is considered good data. Rate this datapoint on a scale from 1 (bad) to 5 (good). ###### " + sentence
+    answer = chat_QwQ(prompt)
+    
+ 
+    # english_text = answer
+    # inputs = tokenizer.encode(english_text, return_tensors="pt", truncation=True)
+    # translated = model.generate(inputs, max_length=40, num_beams=4, early_stopping=True)
+    # chinese_translation = tokenizer.decode(translated[0], skip_special_tokens=True)
+    pprint.pprint(prompt)
+    pprint.pprint(answer)
+    break