156 lines
5.0 KiB
Markdown
156 lines
5.0 KiB
Markdown
---
|
||
library_name: transformers
|
||
license: apache-2.0
|
||
base_model:
|
||
- Qwen/Qwen3-8B
|
||
tags:
|
||
- finance
|
||
- reasoning
|
||
- chain-of-thought
|
||
- financial-analysis
|
||
model-index:
|
||
- name: ODA-Fin-SFT-8B
|
||
results: []
|
||
datasets:
|
||
- OpenDataArena/ODA-Fin-SFT-318k
|
||
language:
|
||
- en
|
||
- zh
|
||
metrics:
|
||
- accuracy
|
||
- f1
|
||
pipeline_tag: question-answering
|
||
---
|
||
|
||
|
||
|
||
<div align="center">
|
||
<h1>Unlocking Data Value in Finance: A Study on Distillation
|
||
and Difficulty-Aware Training</h1>
|
||
|
||
</div>
|
||
|
||
<div align="center">
|
||
|
||
[](https://arxiv.org/abs/2603.07223)
|
||
[](https://huggingface.co/collections/OpenDataArena/oda-finance)
|
||
|
||
</div>
|
||
|
||
<figure align="center">
|
||
<img src="imgs/model_compare.png" width="100%" alt="Model Performance Comparison">
|
||
<figcaption><em>Average score across Financial benchmarks. ODA-Fin-RL/SFT-8B demonstrates strong performance relative to thinking models with significantly more parameters.</em></figcaption>
|
||
</figure>
|
||
|
||
---
|
||
|
||
This repository provides **ODA-Fin-SFT-8B**, a financial language model trained on high-quality Chain-of-Thought data. For the reinforcement learning version, see [ODA-Fin-RL-8B](https://huggingface.co/OpenDataArena/ODA-Fin-RL-8B).
|
||
|
||
## 📖 Overview
|
||
|
||
**ODA-Fin-SFT-8B** is an 8B-parameter financial language model built on Qwen3-8B, fine-tuned on the **ODA-Fin-SFT-318K** dataset—a meticulously curated corpus of 318K samples with high-quality Chain-of-Thought (CoT) reasoning traces distilled from **Qwen3-235B-A22B-Thinking**. This model establishes a robust foundation for financial reasoning, demonstrating state-of-the-art performance across diverse financial tasks.
|
||
|
||
### 🎯 Key Highlights
|
||
|
||
- **Base Model**: Qwen3-8B
|
||
- **Training Data**: ODA-Fin-SFT-318K (318K samples with verified CoT)
|
||
- **Training Method**: Supervised Fine-Tuning with full-parameter updates
|
||
- **Avg Performance**: 72.1% across 9 financial benchmarks
|
||
- **Key Strengths**:
|
||
- Balanced performance across general financial understanding, sentiment analysis, and numerical reasoning
|
||
- Serves as optimal initialization for subsequent RL training
|
||
|
||
---
|
||
|
||
## 🧠 Model Training
|
||
|
||
### Training Configuration
|
||
|
||
```yaml
|
||
Base Model: Qwen/Qwen3-8B
|
||
Training Framework: Full-parameter fine-tuning
|
||
Hardware: 16×NVIDIA A100 (80GB)
|
||
Sequence Length: 16,384 tokens
|
||
Batch Size: 1 per device
|
||
Gradient Accumulation: 16 steps
|
||
Learning Rate: 1.0e-5 (cosine schedule)
|
||
Warmup Ratio: 0.1
|
||
Epochs: 3
|
||
Training Data: ODA-Fin-SFT-318K
|
||
```
|
||
|
||
|
||
---
|
||
|
||
## 📊 Model Performance
|
||
|
||
|
||
Models trained on ODA-Fin-SFT-318K demonstrate superior performance across 9 financial benchmarks:
|
||
|
||
<figure align="center">
|
||
<img src="imgs/main_results_table.png" width="100%" alt="p">
|
||
<figcaption><em>Main Results. 'FinIQ', 'HL' and 'CFQA' refer to FinanceIQ, Headlines, and ConvFinQA benchmarks.</em></figcaption>
|
||
</figure>
|
||
|
||
---
|
||
|
||
|
||
## 📊 Benchmark Details
|
||
|
||
### General Financial Understanding
|
||
|
||
- **FinEval** (Chinese): Financial domain knowledge across banking, insurance, securities (Acc/zh)
|
||
- **Finova**: Agent-level financial reasoning and compliance verification (Acc/zh)
|
||
- **FinanceIQ**: Professional certifications (CPA, CFA) expertise assessment (Acc/zh)
|
||
|
||
### Sentiment Analysis
|
||
|
||
- **FOMC**: Hawkish vs. Dovish monetary policy stance classification (Weighted-F1/en)
|
||
- **FPB**: Financial PhraseBank sentiment classification (Weighted-F1/en)
|
||
- **Headlines**: Financial news headline sentiment interpretation (Weighted-F1/en)
|
||
|
||
### Numerical Reasoning
|
||
|
||
- **FinQA**: Complex numerical reasoning over financial reports (Acc/en)
|
||
- **TaTQA**: Hybrid tabular-textual arithmetic operations (Acc/en)
|
||
- **ConvFinQA**: Multi-turn conversational numerical analysis (Acc/en)
|
||
|
||
---
|
||
|
||
## 📚 Citation
|
||
|
||
```bibtex
|
||
@misc{cao2026unlockingdatavaluefinance,
|
||
title={Unlocking Data Value in Finance: A Study on Distillation and Difficulty-Aware Training},
|
||
author={Chuxue Cao and Honglin Lin and Zhanping Zhong and Xin Gao and Mengzhang Cai and Conghui He and Sirui Han and Lijun Wu},
|
||
year={2026},
|
||
eprint={2603.07223},
|
||
archivePrefix={arXiv},
|
||
primaryClass={cs.LG},
|
||
url={https://arxiv.org/abs/2603.07223},
|
||
}
|
||
```
|
||
|
||
---
|
||
|
||
## 📄 License
|
||
|
||
This model is released under the [Apache 2.0 License](https://opensource.org/licenses/Apache-2.0). The training data (ODA-Fin-SFT-318K) aggregates from 25+ open-source repositories, each with their own licenses.
|
||
|
||
---
|
||
|
||
## 🤝 Acknowledgments
|
||
|
||
We thank the creators of DianJin-R1-Data, Agentar-DeepFinance-100K, financial_phrasebank, Finance-Instruct-500k, and others. We also thank the Qwen team for the powerful Qwen3 series models.
|
||
|
||
---
|
||
|
||
## 🔗 Related Resources
|
||
|
||
- **SFT Dataset**: [ODA-Fin-SFT-318K](https://huggingface.co/datasets/OpenDataArena/ODA-Fin-SFT-318k)
|
||
- **RL Dataset**: [ODA-Fin-RL-12K](https://huggingface.co/datasets/OpenDataArena/ODA-Fin-RL-12K)
|
||
<!-- - **RL Model**: [ODA-Fin-SFT-8B](https://huggingface.co/OpenDataArena/ODA-Fin-SFT-8B) -->
|
||
- **RL Model**: [ODA-Fin-RL-8B](https://huggingface.co/OpenDataArena/ODA-Fin-RL-8B)
|
||
|
||
<!-- - **Paper**: [arXiv:2512.XXXXX](https://arxiv.org/abs/2512.XXXXX) -->
|