SmolLM3-GSM8K-SFT

Fine-tuned version of HuggingFaceTB/SmolLM3-3B-Base optimized for grade school math (GSM8K benchmark).

Performance

Quick Start

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "HuggingFaceTB/SmolLM3-3B-GSM8K-SFT"
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_id)

# Solve a math problem
messages = [{"role": "user", "content": "Janet's ducks lay 16 eggs per day. She eats three for breakfast every morning and bakes muffins for her friends every day with four. She sells the remainder at the farmers' market daily for $2 per fresh duck egg. How much in dollars does she make every day at the farmers' market?"}]

inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to(model.device)
outputs = model.generate(inputs, max_new_tokens=512, do_sample=False)
print(tokenizer.decode(outputs[0], skip_special_tokens=False))

Expected output:

Janet's ducks lay 16 eggs per day.
She eats 3 for breakfast, so 16 - 3 = 13 eggs remain.
She bakes muffins with 4 eggs, so 13 - 4 = 9 eggs remain.
She sells the remaining 9 eggs at $2 each.
9 × $2 = $18

#### 18

Using with vLLM (Recommended for Speed)

from vllm import LLM, SamplingParams

llm = LLM(model="HuggingFaceTB/SmolLM3-3B-GSM8K-SFT")
tokenizer = llm.get_tokenizer()

messages = [{"role": "user", "content": "What is 15 * 23 + 47?"}]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

outputs = llm.generate([prompt], SamplingParams(max_tokens=256, temperature=0))
print(outputs[0].outputs[0].text)

Training Details

Chat Template

This model uses the ChatML format:

You are a helpful AI assistant.
What is 2 + 2?
2 + 2 = 4

#### 4

Training History

We tried multiple approaches to improve math reasoning:

Key finding: More diverse training data (100k vs 50k samples) was more effective than more epochs or GRPO reinforcement learning.

Reproduction

Training and evaluation scripts are available in the training/ folder:

# Train from scratch
python training/train_sft.py

# Evaluate on GSM8K
python training/evaluate_gsm8k.py --model HuggingFaceTB/SmolLM3-3B-GSM8K-SFT --samples 1319

Limitations

• Optimized specifically for grade school math; may not generalize to advanced mathematics
• Best performance with step-by-step reasoning format ending with #### answer
• Context window limited to 2048 tokens during training

Citation

@misc{smollm3-gsm8k-sft,
  title={SmolLM3-3B-GSM8K-SFT: Fine-tuned SmolLM3 for Math Reasoning},
  author={Hugging Face},
  year={2026},
  publisher={Hugging Face},
  url={https://huggingface.co/HuggingFaceTB/SmolLM3-3B-GSM8K-SFT}
}

License

Apache 2.0