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Model: Josephgflowers/Phinance-Phi-4-mini-instruct-finance-v0.4-with-reasoning-gguf
Source: Original Platform

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README.md

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+---
+license: mit
+tags:
+- finance
+- fine-tuning
+- conversational-ai
+- named-entity-recognition
+- sentiment-analysis
+- topic-classification
+- rag
+- multilingual
+- lightweight-llm
+- phi-architecture
+datasets:
+- Josephgflowers/Finance-Instruct-500k
+- GAIR/LIMO
+- TheFinAI/Fino1_Reasoning_Path_FinQA
+- Jarrodbarnes/cortex-1-market-analysis
+- Josephgflowers/Finance_Curriculum_Edu_English
+- Josephgflowers/Finance-Curriculum-Edu-Arabic
+- Josephgflowers/Finance-Curriculum-Edu-Uzbek
+- Josephgflowers/Finance-Curriculum-Edu-Multilingual
+base_model:
+- microsoft/Phi-4-mini-instruct
+- microsoft/Phi-4-mini-reasoning
+---
+
+![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 `<thinking>` or `<think>`.
+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 `<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
+
+```json
+{
+"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."
+}
+````
+
+---
+
+## 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}
+}
+```
+