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## Model Description
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.
## Training Data
- **Source**: HuggingFaceH4/MATH-500 (50 samples).
- **Format**: Prompts with <reasoning>...</reasoning><answer>...</answer> structure.
## Fine-Tuning Process
- **Method**: Incremental SFT with learning rate=1e-5, 1 epoch per batch, batch size=10.
- **Setup**: Google Colab Pro with A100 GPU.
- **Date and Time**: Fine-tuning completed at 10:50 PM IST on Friday, June 06, 2025.
## Usage
To use this model for mathematical reasoning:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("ziadrone/oneplusaries4")
tokenizer = AutoTokenizer.from_pretrained("ziadrone/oneplusaries4")
prompt = "SYSTEM: 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:
1. Carefully analyze the question and identify the key information.
2. Develop a clear and concise plan to approach the problem.
3. Execute your plan step-by-step, providing detailed explanations and intermediate calculations.
4. Verify your solution to ensure it is accurate and makes sense in the context of the problem.
5. Present your final answer in a clear and concise format.
6. Always enclose the reasoning process within <reasoning>...</reasoning> tags.
7. Always enclose the final answer within <answer>...</answer> tags.
8. Do not use any other tags besides <reasoning> and <answer>.
9. Do not include any extra information outside of the reasoning or answer tags.\nUSER: Solve the equation 2x + 3 = 7."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
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