![image/png](https://cdn-uploads.huggingface.co/production/uploads/6328952f798f8d122ce62a44/lcG3xN0mcRPwM248I30CQ.png) # Phinance-Phi-4-mini-instruct-finance-v0.4-with-reasoning ## Overview **Phinance-Phi-4-mini-instruct-finance-v0.4-with-reasoning** is a compact, fine-tuned model built on top of [microsoft/Phi-4-mini-instruct](https://huggingface.co/microsoft/Phi-4-mini-instruct) with a strong emphasis on structured financial reasoning and instruction-following. This release blends financial QA, reasoning chains, and RAG-ready formatting into a lightweight agent optimized for advanced finance applications. This model is particularly good at producing structured outputs like JSON, following instruction patterns, and chaining logical steps when prompted with tags like `` or ``. This model also outperforms the base models on multi language capabilities. --- ## 🔄 **Latest Training Run: Finance Curriculum Reasoning Expansion** After v0.4, the model was further fine-tuned on newly released multilingual finance reasoning datasets, explicitly targeting real-world coverage gaps in non-English finance QA: - [Finance Curriculum Edu English](https://huggingface.co/datasets/Josephgflowers/Finance_Curriculum_Edu_English) - [Finance-Curriculum-Edu-Arabic](https://huggingface.co/datasets/Josephgflowers/Finance-Curriculum-Edu-Arabic) - [Finance-Curriculum-Edu-Uzbek](https://huggingface.co/datasets/Josephgflowers/Finance-Curriculum-Edu-Uzbek) - [Finance-Curriculum-Edu-Multilingual](https://huggingface.co/datasets/Josephgflowers/Finance-Curriculum-Edu-Multilingual) **This training phase addressed:** - Conceptual reasoning and QA coherence across 60+ languages - Robustness to diverse phrasing, financial domains, and real-world curriculum topics - Further reduction of hallucinations and improved answer structure, especially for small and mid-sized LMs in non-English settings --- ## Model Workflow & Training Strategy ### 1. **Initial Fine-Tune** * Base: `Phi-4-mini-instruct` * Dataset: [Finance-Instruct-500k](https://huggingface.co/datasets/Josephgflowers/Finance-Instruct-500k) * Additional datasets: [LIMO](https://huggingface.co/datasets/GAIR/LIMO), [Fin01](https://huggingface.co/datasets/TheFinAI/Fino1_Reasoning_Path_FinQA) ### 2. **Back Merge** * Model was merged back with `Phi-4-mini-instruct` to retain broad instruction capability. ### 3. **Reasoning Augmentation** * Generated question-answer sets from `Finance-Instruct-500k` using reasoning system prompts * Filtered for format quality and signal-to-noise ratio * Trained again on: generated reasoning dataset, LIMO, Fin01 ### 4. **Final Merge** * Merged with `Phi-4-mini-reasoning` to strengthen chain-of-thought behavior ### 5. **Reason Pass** * Trained on: - Filtered reasoning subset of self-generated 500k QA set - [Cortex-1 Market Analysis](https://huggingface.co/datasets/Jarrodbarnes/cortex-1-market-analysis) - LIMO - Fin01 ### 6. **Finance Curriculum Reasoning Expansion (NEW)** * Trained with four new datasets targeting real-world and multilingual finance curriculum QA: * Generated question-answer sets from `Finance-Instruct-500k` using reasoning system prompts * Filtered for format quality and signal-to-noise ratio * Self generated reasoning dataset using finance topics. Human preference optimized. * Trained again on: generated reasoning dataset, LIMO, Fin01 - [Finance Curriculum Edu English](https://huggingface.co/datasets/Josephgflowers/Finance_Curriculum_Edu_English) - [Finance-Curriculum-Edu-Arabic](https://huggingface.co/datasets/Josephgflowers/Finance-Curriculum-Edu-Arabic) - [Finance-Curriculum-Edu-Uzbek](https://huggingface.co/datasets/Josephgflowers/Finance-Curriculum-Edu-Uzbek) - [Finance-Curriculum-Edu-Multilingual](https://huggingface.co/datasets/Josephgflowers/Finance-Curriculum-Edu-Multilingual) --- ## Key Capabilities * **Financial Reasoning**: Great at multi-step reasoning across investment strategies, reports, and economic topics * **Instruction Following**: Precise response formatting with few-shot or system messages * **Multi-Turn Dialogues**: Maintains context across long conversations * **Structured Output**: NER, parsing, and tagging tasks return valid JSON by default * **RAG-Compatible**: Handles prepended external context in the user field * **Tag-Aware**: Supports `` tags to guide reasoning chains * **Multilingual Finance QA**: Expanded coverage in 60+ languages for curriculum-based financial topics --- ## Usage Tips * Use system messages like: ``` You are a financial assistant that explains your reasoning step by step. Use ... to wrap your reasoning. ```` * Expect JSON-style outputs for tasks like: * Entity extraction * Address parsing * XBRL tagging --- ## Example ```json { "system": "You are a financial reasoning assistant. Use to show your steps.", "user": "ABC Inc reported a quarterly revenue increase of 12% while cutting debt by 8%\nWhat does this indicate about the company’s short-term stability?", "assistant": "This revenue increase suggests improved sales or pricing power. Debt reduction enhances cash flow and reduces risk. Together, they signal improved short-term financial health. It indicates strong short-term stability." } ```` --- ## Model Details * **Base**: Phi-4-mini-instruct * **Architecture**: \~3.8B params (mini) * **Version**: v0.4 + Multilingual Curriculum Reasoning Expansion * **License**: MIT * **Framework**: Hugging Face Transformers --- ## Citation ```bibtex @model{josephgflowers2025phinancephi4, title={Phinance-Phi-4-mini-instruct-finance-v0.4-with-reasoning}, author={Joseph G. Flowers}, year={2025}, url={https://huggingface.co/Josephgflowers/Phinance-Phi-4-mini-instruct-finance-v0.4-with-reasoning} } ```