PowerInfer

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SmallThinker-3B-Preview

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PowerInfer 241

This is my evaluation script.

import re
import time
import torch
from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer
from tqdm import tqdm

import re

def extract_final_answer(text):
    patterns = [ 
        r"\*+Final\s+Answer\*+\s*\n*\s*\\\[\s*\\boxed\s*{\s*([0-9.-]+)\s*}\s*\\\]",
        r"\*+Final\s+Answer\*+\s*\n*\s*\\\[\s*([0-9.-]+)\s*\\\]",
        r"\*?Final\s+Answer\*?\s*[:=]\s*([0-9.-]+)",
        r"[Tt]he\s+[Ff]inal\s+[Aa]nswer\s+[Ii]s\s*[:=]?\s*([0-9.-]+)",
        r"[Ff]inal\s+[Aa]nswer\s*[:=]\s*([0-9.-]+)",
    ]   

    # text_normalized = text.replace('\\\\', '\\')
    text_normalized = text
    print(text_normalized)

    for i, pattern in enumerate(patterns):
        match = re.search(pattern, text_normalized, re.DOTALL)
        if match:
            result = match.group(1).strip()
            return result
        else:
            print("Not found match")

    return None

def evaluate_answer(predicted, actual):
    if predicted is None:
        return False
    try:
        pred_val = float(predicted)
        actual_val = float(actual)
        return abs(pred_val - actual_val) < 1e-5
    except:
        return predicted.strip() == actual.strip()

def run_inference(model, tokenizer, question, max_new_tokens=16384):
    prompt = [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": question},
    ]
    
    input_text = tokenizer.apply_chat_template(prompt, tokenize=False, add_generation_prompt=True)
    input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(model.device)
    
    generated_ids = model.generate(
        input_ids,
        max_new_tokens=max_new_tokens,
        use_cache=True
    )
    
    response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
    return response

def main():
    # 1. Load model and tokenizer
    model_path = "saves/qwen2-01/full/sft/checkpoint-44000"  # Replace with your model_path
    model = AutoModelForCausalLM.from_pretrained(
        model_path,
        torch_dtype=torch.float16,
        device_map="cuda"
    )
    tokenizer = AutoTokenizer.from_pretrained(model_path)

    # 2. Load the dataset
    dataset = load_dataset("json", data_files="/home/syx/Qwen2.5-Math/evaluation/data/aime24/test.jsonl")  # Replace with the custom dataset
    eval_dataset = dataset["train"]
    
    # 3. Inference and validation
    results = {
        "correct": 0,
        "total": 0,
        "predictions": []
    }
    
    for item in tqdm(eval_dataset):
        question = item["question"]
        ground_truth = item["answer"] if "answer" in item else None
        
        # Inference
        start_time = time.time()
        response = run_inference(model, tokenizer, question)
        inference_time = time.time() - start_time
        
        # Extract
        predicted_answer = extract_final_answer(response)
        #print(response)
        #print(predicted_answer)
        
        # Evaluate
        is_correct = None
        if ground_truth is not None:
            is_correct = evaluate_answer(predicted_answer, ground_truth)
            print(is_correct)
            results["correct"] += int(is_correct)
            results["total"] += 1
            
        # Save
        results["predictions"].append({
            "question": question,
            "response": response,
            "extracted_answer": predicted_answer,
            "ground_truth": ground_truth,
            "is_correct": is_correct,
            "inference_time": inference_time
        })
        
    # 4. Output 
    if results["total"] > 0:
        accuracy = results["correct"] / results["total"] * 100
        print(f"\nAccuracy: {accuracy:.2f}%")
        print(f"Correct: {results['correct']}/{results['total']}")
    
    # Save to Disk 
    import json
    with open("aime24.jsonl", "w") as f:
        json.dump(results, f, indent=2, ensure_ascii=False)

if __name__ == "__main__":
    main()

This is my evaluation script. I conducted testing based on transformers, using configurations consistent with those in the generation_config.json of the released model.
You can refer to the result folder to get the evaluation log.

Thanks! It might be the answer parsing.

For the full finetune does that include system prompt in the messages? Should I be evaluating with "You are a helpful assistant"?

For the full finetune no system prompt was included, but I did use a system prompt during evaluation.