Fino1 Leaderboard
The Fino1 Leaderboard evaluates the performance of various LLMs, including general-purpose models and reasoning-enhanced models, on complex financial tasks. These tasks, such as mathematical question answering and equation execution, assess an LLMโs ability to perform structured financial reasoning and numerical computation.
- "headers": [
- "Models",
- "Average",
- "FinQA",
- "DM-Simplong",
- "XBRL-Math",
- "Type"
- "data": [
- [
- "GPT-4o",
- 68.24,
- 72.49,
- 60,
- 72.22,
- "Instruction-tuned"
- [
- "GPT-4.5",
- 67.46,
- 68.94,
- 59,
- 74.44,
- "Instruction-tuned"
- [
- "GPT-o1",
- 59.84,
- 49.07,
- 56,
- 74.44,
- "Reasoning-enhanced"
- [
- "GPT-o3-mini",
- 65.51,
- 60.87,
- 59,
- 76.67,
- "Reasoning-enhanced"
- [
- "DeepSeek-V3",
- 67.62,
- 73.2,
- 53,
- 76.67,
- "Instruction-tuned"
- [
- "DeepSeek-R1",
- 68.93,
- 65.13,
- 53,
- 86.67,
- "Reasoning-enhanced"
- [
- "Qwen2.5-72B-Instruct",
- 66.72,
- 73.38,
- 59,
- 67.78,
- "Instruction-tuned"
- [
- "Qwen2.5-72B-Instruct-Math",
- 65.69,
- 69.74,
- 42,
- 83.33,
- "Reasoning-enhanced"
- [
- "Qwen2.5-32B-Instruct",
- 64.89,
- 73.11,
- 56,
- 65.56,
- "Instruction-tuned"
- [
- "DeepSeek-R1-Distill-Llama-70B",
- 68.8,
- 66.73,
- 53,
- 86.67,
- "Reasoning-enhanced"
- [
- "Llama3-70B-Instruct",
- 52.2,
- 58.92,
- 41,
- 56.67,
- "Instruction-tuned"
- [
- "Llama3.1-70B-Instruct",
- 58.17,
- 63.18,
- 48,
- 63.33,
- "Instruction-tuned"
- [
- "Llama3.3-70B-Instruct",
- 64.05,
- 68.15,
- 54,
- 70,
- "Instruction-tuned"
- [
- "DeepSeek-R1-Distill-Qwen-32B",
- 68.97,
- 65.48,
- 55,
- 84.44,
- "Reasoning-enhanced"
- [
- "DeepSeek-R1-Distill-Qwen-14B",
- 63.9,
- 63.27,
- 44,
- 84.44,
- "Reasoning-enhanced"
- [
- "DeepSeek-R1-Distill-Llama-8B",
- 53.36,
- 45.96,
- 33,
- 81.11,
- "Reasoning-enhanced"
- [
- "Llama3-8B-Instruct",
- 39.95,
- 41.97,
- 29,
- 48.89,
- "Instruction-tuned"
- [
- "Llama3.1-8B-Instruct",
- 50.12,
- 54.13,
- 34,
- 62.22,
- "Instruction-tuned"
- [
- "LIMO",
- 56.52,
- 63.44,
- 45,
- 61.11,
- "Reasoning-enhanced"
- [
- "s1-32B",
- 68.08,
- 66.81,
- 53,
- 84.44,
- "Reasoning-enhanced"
- [
- "Fino1-8B",
- 61.03,
- 60.87,
- 40,
- 82.22,
- "Reasoning-enhanced"
- [
- "metadata": null
How it works
We used the framework from https://github.com/The-FinAI/FinBen to do the inference. And evaluation method from https://github.com/yale-nlp/DocMath-Eval are used to evaluate the performance of all models. For more details of the evaluation datasets, please check https://github.com/The-FinAI/Fino1 for more details.
Some good practices before submitting a model
1) Make sure you can load your model and tokenizer using AutoClasses:
from transformers import AutoConfig, AutoModel, AutoTokenizer
config = AutoConfig.from_pretrained("your model name", revision=revision)
model = AutoModel.from_pretrained("your model name", revision=revision)
tokenizer = AutoTokenizer.from_pretrained("your model name", revision=revision)
If this step fails, follow the error messages to debug your model before submitting it. It's likely your model has been improperly uploaded.
Note: make sure your model is public!
Note: if your model needs use_remote_code=True
, we do not support this option yet but we are working on adding it, stay posted!
2) Convert your model weights to safetensors
It's a new format for storing weights which is safer and faster to load and use. It will also allow us to add the number of parameters of your model to the Extended Viewer
!
3) Make sure your model has an open license!
This is a leaderboard for Open LLMs, and we'd love for as many people as possible to know they can use your model ๐ค
4) Fill up your model card
When we add extra information about models to the leaderboard, it will be automatically taken from the model card
In case of model failure
If your model is displayed in the FAILED
category, its execution stopped.
Make sure you have followed the above steps first.
If everything is done, check you can launch the EleutherAIHarness on your model locally, using the above command without modifications (you can add --limit
to limit the number of examples per task).
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