Update README.md

README.md

@@ -1,25 +1,61 @@
 ---
+license: apache-2.0
 datasets:
 - TheFinAI/FinCoT
-library_name: transformers
+language:
+- en
+base_model:
+- Qwen/Qwen3-8B
+pipeline_tag: text-generation
 tags:
-- generated_from_trainer
-- open-r1
-licence: license
+- finance
 ---
+# 🦙 Fino1-8B
 
-# Model Card for None
+**Fin-o1-8B** is a fine-tuned version of **Qwen3-8B**, 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.
 
-This model is a fine-tuned version of [None](https://huggingface.co/None) on the TheFinAI/FinCoT
+## 📌 Model Details  
+- **Model Name**: `Fin-o1-8B`  
+- **Base Model**: `Qwen3-8B`  
+- **Fine-Tuned On**: `TheFinAI/FinCoT` Derived from FinQA, TATQA, DocMath-Eval, Econ-Logic, BizBench-QA, DocFinQA dataset.  
+- **Training Method**: SFT and GRPO  
+- **Objective**: `[Enhance performance on specific tasks such as financial mathemtical reasoning]`  
+- **Tokenizer**: Inherited from `Qwen3-8B`  
 
-## Quick start
+
+## 📊 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 `Fin-o1-8B` with Hugging Face's `transformers` library:
 
 ```python
-from transformers import pipeline
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "TheFinAI/Fin-o1-8B"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
 
-question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
-generator = pipeline("text-generation", model="None", device="cuda")
-output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
-print(output["generated_text"])
+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}
+}