初始化项目，由ModelHub XC社区提供模型

Model: Vilyam888/Code_analyze.1.0
Source: Original Platform

inference.py

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+"""
+Inference code for Code Analyzer Model
+This file enables the "Use this model" button on Hugging Face.
+"""
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+
+def load_model_and_tokenizer(model_name: str):
+    """Load model and tokenizer"""
+    tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+    model = AutoModelForCausalLM.from_pretrained(
+        model_name,
+        torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32,
+        device_map="auto",
+        trust_remote_code=True
+    )
+    return model, tokenizer
+
+
+def build_input(task, code):
+    """Build input in the same format as used during training"""
+    parts = []
+    if task.strip():
+        parts.append(f"Задача:\n{task.strip()}")
+    if code.strip():
+        parts.append(f"Решение (код):\n```python\n{code.strip()}\n```")
+    return "\n\n".join(parts)
+
+
+def generate_response(
+    model,
+    tokenizer,
+    task: str,
+    code: str,
+    max_new_tokens: int = 1024,
+    temperature: float = 0.7,
+    top_p: float = 0.8,
+    top_k: int = 20,
+    repetition_penalty: float = 1.05,
+):
+    """Generate analysis response for task and student code"""
+    # Format input in training style
+    input_text = build_input(task, code)
+    prompt = f"{input_text}\n\nОтвет:\n"
+    
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=max_new_tokens,
+            temperature=temperature,
+            top_p=top_p,
+            top_k=top_k,
+            repetition_penalty=repetition_penalty,
+            do_sample=True,
+            pad_token_id=tokenizer.eos_token_id
+        )
+    
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    # Extract only the answer part
+    if "Ответ:" in response:
+        response = response.split("Ответ:")[-1].strip()
+    
+    return response
+
+
+if __name__ == "__main__":
+    # Example usage
+    import json
+    
+    model_name = "Vilyam888/Code_analyze.1.0"
+    
+    print("Loading model...")
+    model, tokenizer = load_model_and_tokenizer(model_name)
+    
+    # Example: task and student code
+    task = "Напишите функцию, которая принимает список чисел и возвращает сумму всех элементов."
+    code = """def sum_list(numbers):
+    total = 0
+    for num in numbers:
+        total += num
+    return total"""
+    
+    print(f"\nЗадача: {task}")
+    print(f"\nКод студента:\n{code}\n")
+    print("Generating analysis...")
+    
+    response = generate_response(model, tokenizer, task, code)
+    
+    # Try to parse as JSON
+    try:
+        result = json.loads(response)
+        print(f"\nРезультат анализа (JSON):")
+        print(json.dumps(result, ensure_ascii=False, indent=2))
+    except json.JSONDecodeError:
+        print(f"\nРезультат анализа:\n{response}")