Josephgflowers/Phinance-Phi-4-mini-instruct-finance-v0.4-with-reasoning-gguf

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> or <think>. This model also outperforms the base models on multi language capabilities.

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🔄 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

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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
  • Finance Curriculum Edu English
  • Finance-Curriculum-Edu-Arabic
  • Finance-Curriculum-Edu-Uzbek
  • Finance-Curriculum-Edu-Multilingual

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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

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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

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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 company’s 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."
}

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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

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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}
}