---
license: apache-2.0
datasets:
- TheFinAI/Fino1_Reasoning_Path_FinQA_v2
language:
- en
base_model:
- Qwen/Qwen2.5-14B-Instruct
pipeline_tag: text-generation
---
# 🦙 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:

```python
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:
```python
@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}
}