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--- |
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language: |
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- en |
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license: apache-2.0 |
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base_model: HuggingFaceTB/SmolLM3-3B |
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tags: |
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- smollm3 |
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- lora |
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- sft |
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- math-reasoning |
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- gsm8k |
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datasets: |
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- HuggingFaceTB/smoltalk2 |
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pipeline_tag: text-generation |
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--- |
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# SmolLM3-3B-MathReason |
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A math-focused fine-tuned version of SmolLM3-3B, optimized for step-by-step mathematical reasoning and problem solving. |
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## Highlights |
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📚 **Math-First**: Trained on ~7K high-quality math and reasoning samples |
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🧠 **Chain-of-Thought**: Supports `/think` mode for detailed reasoning |
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⚡ **Lightweight**: 3B parameters, runs on consumer GPUs |
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## Training Details |
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| Parameter | Value | |
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|-----------|-------| |
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| Base Model | HuggingFaceTB/SmolLM3-3B | |
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| Method | LoRA (r=16, alpha=32) | |
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| Training Data | ~7K samples | |
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| - OpenThoughts3_1.2M_think | 5,000 (math reasoning) | |
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| - s1k_1.1_think | ~1,000 (high-quality math) | |
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| - smoltalk_everyday_convs | 1,000 (everyday reasoning) | |
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| Epochs | 2 | |
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| Learning Rate | 2e-4 (cosine) | |
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| Effective Batch Size | 16 | |
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## Usage |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model = AutoModelForCausalLM.from_pretrained("real-jiakai/SmolLM3-3B-MathReason") |
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tokenizer = AutoTokenizer.from_pretrained("real-jiakai/SmolLM3-3B-MathReason") |
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messages = [ |
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{"role": "system", "content": "/think"}, # Enable reasoning mode |
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{"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?"} |
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] |
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formatted = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) |
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inputs = tokenizer(formatted, return_tensors="pt") |
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outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.7) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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``` |
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## Intended Use |
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- GSM8K style math problems |
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- Step-by-step problem solving |
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- Educational math tutoring |
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- Arithmetic and algebra reasoning |
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## Limitations |
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- English only |
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- May struggle with very complex multi-step problems |
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- Not designed for factual knowledge retrieval |
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## Training Infrastructure |
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- GPU: NVIDIA A100 |
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- Training Time: ~2 hours |
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- Framework: TRL + PEFT |
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