初始化项目，由ModelHub XC社区提供模型

Model: HuggingFaceTB/qwen3-1.7b-gsm8k-sft
Source: Original Platform

scripts/prepare_combined_data.py

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+#!/usr/bin/env python3
+"""
+Combine GSM8K training data with MetaMathQA GSM-related examples.
+This creates a larger, more diverse training set.
+"""
+
+import json
+import re
+from datasets import load_dataset
+
+def extract_answer_gsm8k(answer_text):
+    """Extract the final numerical answer from GSM8K answer format."""
+    match = re.search(r'####\s*(-?[\d,]+\.?\d*)', answer_text)
+    if match:
+        return match.group(1).replace(',', '')
+    return None
+
+def format_reasoning_gsm8k(answer_text):
+    """Convert GSM8K step-by-step format to thinking format."""
+    reasoning = re.sub(r'####\s*-?[\d,]+\.?\d*\s*$', '', answer_text).strip()
+    reasoning = re.sub(r'<<[^>]+>>', '', reasoning)
+    return reasoning
+
+def extract_answer_metamath(response):
+    """Extract answer from MetaMathQA format (usually ends with boxed answer)."""
+    # Try to find boxed answer
+    match = re.search(r'\\boxed\{([^}]+)\}', response)
+    if match:
+        return match.group(1).strip()
+    # Try to find "the answer is X" pattern
+    match = re.search(r'the answer is[:\s]*\$?(-?[\d,]+\.?\d*)', response, re.IGNORECASE)
+    if match:
+        return match.group(1).replace(',', '')
+    # Try to find "= X" at the end
+    match = re.search(r'=\s*\$?(-?[\d,]+\.?\d*)\s*(?:dollars?|\.)?$', response)
+    if match:
+        return match.group(1).replace(',', '')
+    return None
+
+def create_gsm8k_example(question, answer):
+    """Create a training example from GSM8K format."""
+    final_answer = extract_answer_gsm8k(answer)
+    reasoning = format_reasoning_gsm8k(answer)
+    
+    if final_answer is None:
+        return None
+    
+    assistant_content = f"""<think>
+Let me solve this step by step.
+
+{reasoning}
+
+Therefore, the answer is {final_answer}.
+</think>
+
+The answer is {final_answer}"""
+    
+    return {
+        "messages": [
+            {"role": "user", "content": f"Solve the following math problem step by step. Show your reasoning and then provide the final answer.\n\n{question}"},
+            {"role": "assistant", "content": assistant_content}
+        ]
+    }
+
+def create_metamath_example(query, response):
+    """Create a training example from MetaMathQA format."""
+    # Clean up the response - remove LaTeX formatting artifacts
+    clean_response = response.replace('\\n', '\n').strip()
+    
+    # Extract the answer
+    final_answer = extract_answer_metamath(clean_response)
+    if final_answer is None:
+        return None
+    
+    # Remove the boxed answer and everything after for reasoning
+    reasoning = re.sub(r'\\boxed\{[^}]+\}.*$', '', clean_response, flags=re.DOTALL).strip()
+    reasoning = re.sub(r'The answer is.*$', '', reasoning, flags=re.IGNORECASE | re.DOTALL).strip()
+    
+    # Skip if reasoning is too short
+    if len(reasoning) < 50:
+        return None
+    
+    assistant_content = f"""<think>
+Let me solve this step by step.
+
+{reasoning}
+
+Therefore, the answer is {final_answer}.
+</think>
+
+The answer is {final_answer}"""
+    
+    return {
+        "messages": [
+            {"role": "user", "content": f"Solve the following math problem step by step. Show your reasoning and then provide the final answer.\n\n{query}"},
+            {"role": "assistant", "content": assistant_content}
+        ]
+    }
+
+def main():
+    training_data = []
+    
+    # Load GSM8K training data
+    print("Loading GSM8K dataset...")
+    gsm8k = load_dataset("openai/gsm8k", "main", split="train")
+    print(f"Loaded {len(gsm8k)} GSM8K examples")
+    
+    gsm8k_count = 0
+    for example in gsm8k:
+        formatted = create_gsm8k_example(example['question'], example['answer'])
+        if formatted:
+            training_data.append(formatted)
+            gsm8k_count += 1
+    print(f"Added {gsm8k_count} GSM8K examples")
+    
+    # Load MetaMathQA - only GSM-related examples
+    print("\nLoading MetaMathQA dataset...")
+    metamath = load_dataset("meta-math/MetaMathQA", split="train")
+    print(f"Loaded {len(metamath)} MetaMathQA examples")
+    
+    # Filter for GSM-related examples only
+    metamath_count = 0
+    for example in metamath:
+        if 'GSM' in example['type']:  # GSM_Rephrased, GSM_SV, GSM_AnsAug, etc.
+            formatted = create_metamath_example(example['query'], example['response'])
+            if formatted:
+                training_data.append(formatted)
+                metamath_count += 1
+    print(f"Added {metamath_count} MetaMathQA GSM examples")
+    
+    print(f"\nTotal training examples: {len(training_data)}")
+    
+    # Shuffle the data
+    import random
+    random.seed(42)
+    random.shuffle(training_data)
+    
+    # Save to JSONL
+    output_file = "combined_math_train.jsonl"
+    with open(output_file, 'w') as f:
+        for item in training_data:
+            f.write(json.dumps(item) + '\n')
+    
+    print(f"Saved to {output_file}")
+    
+    # Show samples
+    print("\n=== Sample GSM8K example ===")
+    for item in training_data[:10]:
+        if "Natalia" in item['messages'][0]['content']:
+            print(json.dumps(item, indent=2)[:500])
+            break
+
+if __name__ == "__main__":
+    main()