ziadrone / oneplusaries4

ToT-Reasoner-Qwen3-1.7B

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 ...... 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:

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))