SmolLM3-3B-GSM8K-SFT

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))

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

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)

<|im_start|>system
You are a helpful AI assistant.<|im_end|>
<|im_start|>user
What is 2 + 2?<|im_end|>
<|im_start|>assistant
2 + 2 = 4

#### 4<|im_end|>

# Train from scratch
python training/train_sft.py

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

@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}
}