---
language:
- en
license: apache-2.0
base_model: HuggingFaceTB/SmolLM3-3B
tags:
- smollm3
- lora
- sft
- math-reasoning
- gsm8k
datasets:
- HuggingFaceTB/smoltalk2
pipeline_tag: text-generation
---

# SmolLM3-3B-MathReason

A math-focused fine-tuned version of SmolLM3-3B, optimized for step-by-step mathematical reasoning and problem solving.

## Highlights

📚 **Math-First**: Trained on ~7K high-quality math and reasoning samples

🧠 **Chain-of-Thought**: Supports `/think` mode for detailed reasoning

⚡ **Lightweight**: 3B parameters, runs on consumer GPUs

## Training Details

| Parameter | Value |
|-----------|-------|
| Base Model | HuggingFaceTB/SmolLM3-3B |
| Method | LoRA (r=16, alpha=32) |
| Training Data | ~7K samples |
| - OpenThoughts3_1.2M_think | 5,000 (math reasoning) |
| - s1k_1.1_think | ~1,000 (high-quality math) |
| - smoltalk_everyday_convs | 1,000 (everyday reasoning) |
| Epochs | 2 |
| Learning Rate | 2e-4 (cosine) |
| Effective Batch Size | 16 |

## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("real-jiakai/SmolLM3-3B-MathReason")
tokenizer = AutoTokenizer.from_pretrained("real-jiakai/SmolLM3-3B-MathReason")

messages = [
    {"role": "system", "content": "/think"},  # Enable reasoning mode
    {"role": "user", "content": "A store sells apples for $2 each. If John buys 5 apples and pays with a $20 bill, how much change does he get?"}
]

formatted = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(formatted, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Intended Use

- GSM8K style math problems
- Step-by-step problem solving
- Educational math tutoring
- Arithmetic and algebra reasoning

## Limitations

- English only
- May struggle with very complex multi-step problems
- Not designed for factual knowledge retrieval

## Training Infrastructure

- GPU: NVIDIA A100
- Training Time: ~2 hours
- Framework: TRL + PEFT