Fino1-8B
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Fino1-8B is a financial reasoning model fine-tuned from Llama-3.1-8B-Instruct on GPT-4o-generated financial reasoning paths derived from FinQA, followed by 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
| Base model | meta-llama/Llama-3.1-8B-Instruct |
| Architecture | LlamaForCausalLM, 32 layers, hidden size 4096 |
| Precision | bfloat16 |
| Context length | 131,072 tokens (max_position_embeddings) |
| Training data | TheFinAI/Fino1_Reasoning_Path_FinQA |
| Training method | SFT on GPT-4o reasoning paths, then RL |
| Language | English |
| License | Llama 3.1 Community License (inherited from the base model) |
Quick Start
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "TheFinAI/Fino1-8B"
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.