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Model: TheFinAI/Fino1-8B
Source: Original Platform

README.md

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+---
+license: llama3.1
+datasets:
+- TheFinAI/Fino1_Reasoning_Path_FinQA
+language:
+- en
+base_model:
+- meta-llama/Llama-3.1-8B-Instruct
+pipeline_tag: text-generation
+---
+# 🦙 Fino1-8B
+
+**Fino1-8B** is a fine-tuned version of **Llama 3.1 8B Instruct**, designed to improve performance on **[financial reasoning tasks]**. This model has been trained using **SFT** and **RF** on **TheFinAI/Fino1_Reasoning_Path_FinQA**, enhancing its capabilities in **financial reasoning tasks**.
+Check our paper arxiv.org/abs/2502.08127 for more details.
+
+## 📌 Model Details  
+- **Model Name**: `Fino1-8B`  
+- **Base Model**: `Meta Llama 3.1 8B Instruct`  
+- **Fine-Tuned On**: `TheFinAI/Fino1_Reasoning_Path_FinQA` Derived from FinQA dataset.  
+- **Training Method**: SFT and RF  
+- **Objective**: `[Enhance performance on specific tasks such as financial mathemtical reasoning]`  
+- **Tokenizer**: Inherited from `Llama 3.1 8B 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 `Fino1-8B` with Hugging Face's `transformers` library:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "TheFinAI/Fino1-8B"
+
+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}
+}