🦙 Fino1-14B

Fino1-14B is a fine-tuned version of Qwen2.5-14B-Instruct, designed to improve performance on [financial reasoning tasks]. This model has been trained using SFT and RF on TheFinAI/Fino1_Reasoning_Path_FinQA_v2, enhancing its capabilities in financial reasoning tasks. Check our paper arxiv.org/abs/2502.08127 for more details.

📌 Model Details

• Model Name: Fino1-14B
• Base Model: Qwen2.5-14B-Instruct
• Fine-Tuned On: TheFinAI/Fino1_Reasoning_Path_FinQA_v2 Derived from multiple financial dataset.
• Training Method: SFT and RF
• Objective: [Enhance performance on specific tasks such as financial mathemtical reasoning]
• Tokenizer: Inherited from Qwen/Qwen2.5-14B-Instruct

📊 Training Configuration

• Training Hardware: GPU: [e.g., 4xH100]
• Batch Size: [e.g., 16]
• Learning Rate: [e.g., 2e-5]
• Epochs: [e.g., 3]
• Optimizer: [e.g., AdamW, LAMB]

🔧 Usage

To use Fino1-14B with Hugging Face's transformers library:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "TheFinAI/Fino1-14B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What is the results of 3-5?"
inputs = tokenizer(input_text, return_tensors="pt")

output = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(output[0], skip_special_tokens=True))

💡 Citation

If you use this model in your research, please cite:

@article{qian2025fino1,
  title={Fino1: On the Transferability of Reasoning Enhanced LLMs to Finance},
  author={Qian, Lingfei and Zhou, Weipeng and Wang, Yan and Peng, Xueqing and Huang, Jimin and Xie, Qianqian},
  journal={arXiv preprint arXiv:2502.08127},
  year={2025}
}