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--- |
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language: en |
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license: apache-2.0 |
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library_name: transformers |
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tags: |
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- text-generation |
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- mathematics |
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- reasoning |
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- qwen |
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- sft |
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- fine-tune |
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base_model: Qwen/Qwen3-1.7B |
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datasets: |
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- HuggingFaceH4/MATH-500 |
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--- |
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# ToT-Reasoner-Qwen3-1.7B |
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## Model Description |
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This model is a fine-tuned version of `Qwen/Qwen3-1.7B` using Supervised Fine-Tuning (SFT) on the `HuggingFaceH4/MATH-500` dataset. It is optimized for mathematical reasoning and problem-solving tasks. The fine-tuning process was performed by EKAGRATA TECH PRIVATE LIMITED. |
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## Training Data |
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- **Source**: `HuggingFaceH4/MATH-500` (50 samples). |
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- **Format**: Prompts with `<reasoning>...</reasoning><answer>...</answer>` structure. |
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## Fine-Tuning Process |
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- **Method**: Incremental SFT with learning rate=1e-5, 1 epoch per batch, batch size=10. |
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- **Setup**: Google Colab Pro with A100 GPU. |
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- **Date and Time**: Model uploaded at 07:23 AM on Friday, July 04, 2025. |
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## Usage |
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To use this model for mathematical reasoning: |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model = AutoModelForCausalLM.from_pretrained("ziadrone/oneplusaries55") |
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tokenizer = AutoTokenizer.from_pretrained("ziadrone/oneplusaries55") |
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SYSTEM_PROMPT = """You are a large language model trained to solve mathematical, logical, physics, and general reasoning problems. You must follow the following steps to solve the problem: |
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1. Carefully analyze the question and identify the key information. |
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2. Develop a clear and concise plan to approach the problem. |
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3. Execute your plan step-by-step, providing detailed explanations and intermediate calculations. |
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4. Verify your solution to ensure it is accurate and makes sense in the context of the problem. |
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5. Present your final answer in a clear and concise format. |
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6. Always enclose the reasoning process within <reasoning>...</reasoning> tags. |
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7. Always enclose the final answer within <answer>...</answer> tags. |
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8. Do not use any other tags besides <reasoning> and <answer>. |
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9. Do not include any extra information outside of the reasoning or answer tags.""" |
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prompt = f"SYSTEM: {SYSTEM_PROMPT}\nUSER: Solve the equation 2x + 3 = 7." |
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inputs = tokenizer(prompt, return_tensors="pt") |
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outputs = model.generate(**inputs, max_length=512) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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``` |
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## Performance |
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This model has been fine-tuned on mathematical reasoning tasks and should perform well on similar problems involving step-by-step logical reasoning. |
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## Limitations |
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- The model was trained on a limited dataset (50 samples) |
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- Performance may vary on problems significantly different from the training data |
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- Always verify mathematical results for critical applications |
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## License |
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This model is released under the Apache 2.0 license. |
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