README.md

metadata

license: apache-2.0
base_model: HuggingFaceTB/SmolLM3-3B-Base
tags:
  - math
  - gsm8k
  - sft
  - fine-tuned
  - reasoning
datasets:
  - meta-math/MetaMathQA
language:
  - en
pipeline_tag: text-generation
metrics:
  - accuracy
model-index:
  - name: SmolLM3-3B-GSM8K-SFT
    results:
      - task:
          type: text-generation
          name: Math Reasoning
        dataset:
          name: GSM8K
          type: openai/gsm8k
        metrics:
          - type: accuracy
            value: 65.8
            name: GSM8K Accuracy

SmolLM3-3B-GSM8K-SFT

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

Performance

Metric	Score
GSM8K Accuracy	65.8%
Baseline (SmolLM3-3B-Base)	23.3%
Improvement	+42.5 pp (2.8x)

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

Parameter	Value
Base Model	HuggingFaceTB/SmolLM3-3B-Base
Training Data	MetaMathQA (100k samples)
Method	Supervised Fine-Tuning (SFT) with TRL 1.0.0
Hardware	NVIDIA H100 80GB
Training Time	~3h 16min
Epochs	1
Batch Size	2 (effective 16 with gradient accumulation)
Learning Rate	1e-5
Max Sequence Length	2048
Optimizer	AdamW

Chat Template

This model uses the ChatML format:

<|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|>

Training History

We tried multiple approaches to improve math reasoning:

Stage	GSM8K Accuracy	Method	Notes
Baseline	23.3%	-	SmolLM3-3B-Base with no training
SFT V1	59.6%	SFT 2 epochs	MetaMathQA 50k samples
GRPO	58%	GRPO	GSM8K train set - ineffective
SFT V2	65.8%	SFT 1 epoch	MetaMathQA 100k samples ✓

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