Fin-o1-14B

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Fin-o1-14B is a financial reasoning model fine-tuned from Qwen3-14B with supervised fine-tuning on financial chain-of-thought data followed by GRPO reinforcement learning. It was introduced in Fino1: On the Transferability of Reasoning-Enhanced LLMs and Reinforcement Learning to Finance (arXiv:2502.08127).

[!NOTE] Fin-o1 vs. Fino1. The two families come from the same paper but are different generations:

	Fino1 (2025-02/03)	Fin-o1 (2025-05)
8B	Fino1-8B — Llama-3.1-8B-Instruct	Fin-o1-8B — Qwen3-8B
14B	Fino1-14B — Qwen2.5-14B-Instruct	Fin-o1-14B — Qwen3-14B
Training	SFT + RL on GPT-4o reasoning paths	SFT + GRPO

For new work we recommend the Fin-o1 models.

Model Details

Quick Start

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "TheFinAI/Fin-o1-14B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")

messages = [{
    "role": "user",
    "content": "A company's revenue grew from $120M to $150M while operating costs rose from $90M to $105M. "
               "By how many percentage points did the operating margin change?",
}]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt", return_dict=True).to(model.device)
output = model.generate(**inputs, max_new_tokens=1024)
print(tokenizer.decode(output[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

Evaluation

Fino1 and Fin-o1 are evaluated on FinQA, DocMath-Eval (simple / complex long), XBRL-Math and related financial reasoning tasks. See the paper and the Open FinLLM Reasoning Leaderboard for results.

Intended Use & Limitations

• Intended use: research on financial numerical reasoning, question answering over financial text and tables, and as a baseline for financial reasoning models.
• Not intended for: investment advice, trading decisions, or any automated financial decision without human review.
• The model can produce incorrect calculations or hallucinated figures, especially on long documents with multiple tables.
• Trained and evaluated on English data only.

Citation

@misc{qian2025fino1transferabilityreasoningenhancedllms,
      title={Fino1: On the Transferability of Reasoning-Enhanced LLMs and Reinforcement Learning to Finance},
      author={Lingfei Qian and Weipeng Zhou and Yan Wang and Xueqing Peng and Han Yi and Yilun Zhao and Jimin Huang and Qianqian Xie and Jian-yun Nie},
      year={2025},
      eprint={2502.08127},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2502.08127},
}

Contact

Questions and issues: open a discussion on this repository or on GitHub.