README.md

metadata

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

Unlocking Data Value in Finance: A Study on Distillation and Difficulty-Aware Training

Average score across Financial benchmarks. ODA-Fin-RL/SFT-8B demonstrates strong performance relative to thinking models with significantly more parameters.

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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.

📖 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

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🧠 Model Training

Training Configuration

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

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📊 Model Performance

Models trained on ODA-Fin-SFT-318K demonstrate superior performance across 9 financial benchmarks:

Main Results. 'FinIQ', 'HL' and 'CFQA' refer to FinanceIQ, Headlines, and ConvFinQA benchmarks.

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📊 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)

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📚 Citation

@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}, 
}

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📄 License

This model is released under the Apache 2.0 License. The training data (ODA-Fin-SFT-318K) aggregates from 25+ open-source repositories, each with their own licenses.

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🤝 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.

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🔗 Related Resources

• SFT Dataset: ODA-Fin-SFT-318K
• RL Dataset: ODA-Fin-RL-12K
• RL Model: ODA-Fin-RL-8B