Text Generation
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- ---
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- license: apache-2.0
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- datasets:
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- - TheFinAI/Fino1_Reasoning_Path_FinQA_v2
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- language:
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- - en
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- base_model:
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- - Qwen/Qwen2.5-14B-Instruct
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- pipeline_tag: text-generation
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- ---
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- # 🦙 Fino1-14B
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-
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- **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**.
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- Check our paper arxiv.org/abs/2502.08127 for more details.
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-
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- ## 📌 Model Details
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- - **Model Name**: `Fino1-14B`
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- - **Base Model**: `Qwen2.5-14B-Instruct`
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- - **Fine-Tuned On**: `TheFinAI/Fino1_Reasoning_Path_FinQA_v2` Derived from multiple financial dataset.
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- - **Training Method**: SFT and RF
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- - **Objective**: `[Enhance performance on specific tasks such as financial mathemtical reasoning]`
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- - **Tokenizer**: Inherited from `Qwen/Qwen2.5-14B-Instruct`
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-
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-
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- ## 📊 Training Configuration
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- - **Training Hardware**: `GPU: [e.g., 4xH100]`
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- - **Batch Size**: `[e.g., 16]`
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- - **Learning Rate**: `[e.g., 2e-5]`
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- - **Epochs**: `[e.g., 3]`
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- - **Optimizer**: `[e.g., AdamW, LAMB]`
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-
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- ## 🔧 Usage
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- To use `Fino1-14B` with Hugging Face's `transformers` library:
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-
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- ```python
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- from transformers import AutoModelForCausalLM, AutoTokenizer
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-
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- model_name = "TheFinAI/Fino1-14B"
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-
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- tokenizer = AutoTokenizer.from_pretrained(model_name)
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- model = AutoModelForCausalLM.from_pretrained(model_name)
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-
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- input_text = "What is the results of 3-5?"
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- inputs = tokenizer(input_text, return_tensors="pt")
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-
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- output = model.generate(**inputs, max_new_tokens=200)
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- print(tokenizer.decode(output[0], skip_special_tokens=True))
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- ```
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-
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- ## 💡 Citation
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-
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- If you use this model in your research, please cite:
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- ```python
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- @article{qian2025fino1,
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- title={Fino1: On the Transferability of Reasoning Enhanced LLMs to Finance},
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- author={Qian, Lingfei and Zhou, Weipeng and Wang, Yan and Peng, Xueqing and Huang, Jimin and Xie, Qianqian},
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- journal={arXiv preprint arXiv:2502.08127},
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- year={2025}
 
 
 
 
 
 
 
 
 
 
 
 
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  }
 
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+ ---
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+ license: apache-2.0
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+ datasets:
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+ - TheFinAI/Fino1_Reasoning_Path_FinQA_v2
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+ language:
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+ - zho
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+ - eng
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+ - fra
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+ - spa
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+ - por
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+ - deu
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+ - ita
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+ - rus
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+ - jpn
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+ - kor
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+ - vie
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+ - tha
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+ - ara
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+ base_model:
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+ - Qwen/Qwen2.5-14B-Instruct
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+ pipeline_tag: text-generation
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+ ---
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+ # 🦙 Fino1-14B
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+
25
+ **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**.
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+ Check our paper arxiv.org/abs/2502.08127 for more details.
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+
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+ ## 📌 Model Details
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+ - **Model Name**: `Fino1-14B`
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+ - **Base Model**: `Qwen2.5-14B-Instruct`
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+ - **Fine-Tuned On**: `TheFinAI/Fino1_Reasoning_Path_FinQA_v2` Derived from multiple financial dataset.
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+ - **Training Method**: SFT and RF
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+ - **Objective**: `[Enhance performance on specific tasks such as financial mathemtical reasoning]`
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+ - **Tokenizer**: Inherited from `Qwen/Qwen2.5-14B-Instruct`
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+
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+
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+ ## 📊 Training Configuration
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+ - **Training Hardware**: `GPU: [e.g., 4xH100]`
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+ - **Batch Size**: `[e.g., 16]`
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+ - **Learning Rate**: `[e.g., 2e-5]`
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+ - **Epochs**: `[e.g., 3]`
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+ - **Optimizer**: `[e.g., AdamW, LAMB]`
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+
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+ ## 🔧 Usage
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+ To use `Fino1-14B` with Hugging Face's `transformers` library:
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+
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+ ```python
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+ from transformers import AutoModelForCausalLM, AutoTokenizer
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+
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+ model_name = "TheFinAI/Fino1-14B"
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+
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+ tokenizer = AutoTokenizer.from_pretrained(model_name)
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+ model = AutoModelForCausalLM.from_pretrained(model_name)
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+
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+ input_text = "What is the results of 3-5?"
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+ inputs = tokenizer(input_text, return_tensors="pt")
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+
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+ output = model.generate(**inputs, max_new_tokens=200)
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+ print(tokenizer.decode(output[0], skip_special_tokens=True))
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+ ```
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+
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+ ## 💡 Citation
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+
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+ If you use this model in your research, please cite:
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+ ```python
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+ @article{qian2025fino1,
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+ title={Fino1: On the Transferability of Reasoning Enhanced LLMs to Finance},
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+ author={Qian, Lingfei and Zhou, Weipeng and Wang, Yan and Peng, Xueqing and Huang, Jimin and Xie, Qianqian},
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+ journal={arXiv preprint arXiv:2502.08127},
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+ year={2025}
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  }