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 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 <thinking>
.
This model also outperforms the base models on multi language capabilities.
π 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
- Finance-Curriculum-Edu-Arabic
- Finance-Curriculum-Edu-Uzbek
- 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
- Additional datasets: LIMO, Fin01
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
- 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
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
<thinking>
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 <thinking>...</thinking> to wrap your reasoning.
Expect JSON-style outputs for tasks like:
Entity extraction
Address parsing
XBRL tagging
Example
{
"system": "You are a financial reasoning assistant. Use <thinking> to show your steps.",
"user": "<context>ABC Inc reported a quarterly revenue increase of 12% while cutting debt by 8%</context>\nWhat does this indicate about the companyβs short-term stability?",
"assistant": "<thinking>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.</thinking> 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
@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}
}
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Base model
microsoft/Phi-4-mini-instruct