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Model: HuggingFaceTB/SmolLM3-3B-GSM8K-SFT
Source: Original Platform

training/evaluate_gsm8k.py

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+#!/usr/bin/env python3
+"""
+Simple GSM8K evaluation script for SmolLM3-3B-GSM8K-SFT
+
+Requirements:
+    pip install transformers datasets vllm
+
+Usage:
+    python evaluate_gsm8k.py --model HuggingFaceTB/SmolLM3-3B-GSM8K-SFT --samples 100
+"""
+
+import argparse
+import re
+from datasets import load_dataset
+from vllm import LLM, SamplingParams
+
+def extract_answer(text: str) -> str:
+    """Extract numerical answer from model output."""
+    # Look for #### followed by number
+    match = re.search(r'####\s*(-?[\d,]+(?:\.\d+)?)', text)
+    if match:
+        return match.group(1).replace(',', '')
+    
+    # Look for "answer is X" pattern
+    match = re.search(r'answer is[:\s]*(-?[\d,]+(?:\.\d+)?)', text.lower())
+    if match:
+        return match.group(1).replace(',', '')
+    
+    # Extract last number
+    numbers = re.findall(r'-?[\d,]+(?:\.\d+)?', text)
+    if numbers:
+        return numbers[-1].replace(',', '')
+    
+    return ""
+
+def extract_gold_answer(text: str) -> str:
+    """Extract gold answer from GSM8K format."""
+    match = re.search(r'####\s*(-?[\d,]+(?:\.\d+)?)', text)
+    if match:
+        return match.group(1).replace(',', '')
+    return ""
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model", type=str, default="HuggingFaceTB/SmolLM3-3B-GSM8K-SFT")
+    parser.add_argument("--samples", type=int, default=100)
+    args = parser.parse_args()
+    
+    print(f"Loading model: {args.model}")
+    llm = LLM(
+        model=args.model,
+        trust_remote_code=True,
+        gpu_memory_utilization=0.9,
+    )
+    
+    tokenizer = llm.get_tokenizer()
+    sampling_params = SamplingParams(
+        max_tokens=512,
+        temperature=0.0,
+        stop=["<|im_end|>"],
+    )
+    
+    print("Loading GSM8K test set...")
+    dataset = load_dataset("openai/gsm8k", "main", split="test")
+    dataset = dataset.select(range(min(args.samples, len(dataset))))
+    
+    # Prepare prompts
+    prompts = []
+    for example in dataset:
+        messages = [{"role": "user", "content": example["question"]}]
+        prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+        prompts.append(prompt)
+    
+    # Generate
+    print(f"Evaluating {len(prompts)} samples...")
+    outputs = llm.generate(prompts, sampling_params)
+    
+    # Score
+    correct = 0
+    for i, (example, output) in enumerate(zip(dataset, outputs)):
+        pred = extract_answer(output.outputs[0].text)
+        gold = extract_gold_answer(example["answer"])
+        
+        if pred == gold:
+            correct += 1
+    
+    accuracy = correct / len(dataset) * 100
+    print(f"\nResults:")
+    print(f"  Correct: {correct}/{len(dataset)}")
+    print(f"  Accuracy: {accuracy:.1f}%")
+
+if __name__ == "__main__":
+    main()