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Model: Josephgflowers/Phinance-Phi-4-mini-instruct-finance-v0.4-with-reasoning-gguf Source: Original Platform
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Josephgflowers--Phinance-Phi-4-mini-instruct-finance-v0.4-with-reasoning-f16.gguf filter=lfs diff=lfs merge=lfs -text
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Josephgflowers--Phinance-Phi-4-mini-instruct-finance-v0.4-with-reasoning-q8_0.gguf filter=lfs diff=lfs merge=lfs -text
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---
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license: mit
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tags:
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- finance
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- fine-tuning
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- conversational-ai
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- named-entity-recognition
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- sentiment-analysis
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- topic-classification
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- rag
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- multilingual
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- lightweight-llm
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- phi-architecture
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datasets:
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- Josephgflowers/Finance-Instruct-500k
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- GAIR/LIMO
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- TheFinAI/Fino1_Reasoning_Path_FinQA
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- Jarrodbarnes/cortex-1-market-analysis
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- Josephgflowers/Finance_Curriculum_Edu_English
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- Josephgflowers/Finance-Curriculum-Edu-Arabic
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- Josephgflowers/Finance-Curriculum-Edu-Uzbek
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- Josephgflowers/Finance-Curriculum-Edu-Multilingual
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base_model:
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- microsoft/Phi-4-mini-instruct
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- microsoft/Phi-4-mini-reasoning
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---
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# Phinance-Phi-4-mini-instruct-finance-v0.4-with-reasoning
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## Overview
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**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.
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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>`.
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This model also outperforms the base models on multi language capabilities.
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---
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## 🔄 **Latest Training Run: Finance Curriculum Reasoning Expansion**
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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:
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- [Finance Curriculum Edu English](https://huggingface.co/datasets/Josephgflowers/Finance_Curriculum_Edu_English)
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- [Finance-Curriculum-Edu-Arabic](https://huggingface.co/datasets/Josephgflowers/Finance-Curriculum-Edu-Arabic)
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- [Finance-Curriculum-Edu-Uzbek](https://huggingface.co/datasets/Josephgflowers/Finance-Curriculum-Edu-Uzbek)
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- [Finance-Curriculum-Edu-Multilingual](https://huggingface.co/datasets/Josephgflowers/Finance-Curriculum-Edu-Multilingual)
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**This training phase addressed:**
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- Conceptual reasoning and QA coherence across 60+ languages
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- Robustness to diverse phrasing, financial domains, and real-world curriculum topics
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- Further reduction of hallucinations and improved answer structure, especially for small and mid-sized LMs in non-English settings
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---
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## Model Workflow & Training Strategy
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### 1. **Initial Fine-Tune**
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* Base: `Phi-4-mini-instruct`
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* Dataset: [Finance-Instruct-500k](https://huggingface.co/datasets/Josephgflowers/Finance-Instruct-500k)
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* Additional datasets: [LIMO](https://huggingface.co/datasets/GAIR/LIMO), [Fin01](https://huggingface.co/datasets/TheFinAI/Fino1_Reasoning_Path_FinQA)
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### 2. **Back Merge**
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* Model was merged back with `Phi-4-mini-instruct` to retain broad instruction capability.
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### 3. **Reasoning Augmentation**
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* Generated question-answer sets from `Finance-Instruct-500k` using reasoning system prompts
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* Filtered for format quality and signal-to-noise ratio
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* Trained again on: generated reasoning dataset, LIMO, Fin01
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### 4. **Final Merge**
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* Merged with `Phi-4-mini-reasoning` to strengthen chain-of-thought behavior
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### 5. **Reason Pass**
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* Trained on:
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- Filtered reasoning subset of self-generated 500k QA set
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- [Cortex-1 Market Analysis](https://huggingface.co/datasets/Jarrodbarnes/cortex-1-market-analysis)
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- LIMO
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- Fin01
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### 6. **Finance Curriculum Reasoning Expansion (NEW)**
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* Trained with four new datasets targeting real-world and multilingual finance curriculum QA:
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* Generated question-answer sets from `Finance-Instruct-500k` using reasoning system prompts
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* Filtered for format quality and signal-to-noise ratio
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* Self generated reasoning dataset using finance topics. Human preference optimized.
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* Trained again on: generated reasoning dataset, LIMO, Fin01
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- [Finance Curriculum Edu English](https://huggingface.co/datasets/Josephgflowers/Finance_Curriculum_Edu_English)
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- [Finance-Curriculum-Edu-Arabic](https://huggingface.co/datasets/Josephgflowers/Finance-Curriculum-Edu-Arabic)
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- [Finance-Curriculum-Edu-Uzbek](https://huggingface.co/datasets/Josephgflowers/Finance-Curriculum-Edu-Uzbek)
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- [Finance-Curriculum-Edu-Multilingual](https://huggingface.co/datasets/Josephgflowers/Finance-Curriculum-Edu-Multilingual)
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---
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## Key Capabilities
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* **Financial Reasoning**: Great at multi-step reasoning across investment strategies, reports, and economic topics
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* **Instruction Following**: Precise response formatting with few-shot or system messages
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* **Multi-Turn Dialogues**: Maintains context across long conversations
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* **Structured Output**: NER, parsing, and tagging tasks return valid JSON by default
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* **RAG-Compatible**: Handles prepended external context in the user field
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* **Tag-Aware**: Supports `<thinking>` tags to guide reasoning chains
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* **Multilingual Finance QA**: Expanded coverage in 60+ languages for curriculum-based financial topics
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---
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## Usage Tips
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* Use system messages like:
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```
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You are a financial assistant that explains your reasoning step by step. Use <thinking>...</thinking> to wrap your reasoning.
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````
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* Expect JSON-style outputs for tasks like:
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* Entity extraction
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* Address parsing
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* XBRL tagging
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---
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## Example
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```json
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{
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"system": "You are a financial reasoning assistant. Use <thinking> to show your steps.",
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"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?",
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"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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}
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````
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---
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## Model Details
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* **Base**: Phi-4-mini-instruct
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* **Architecture**: \~3.8B params (mini)
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* **Version**: v0.4 + Multilingual Curriculum Reasoning Expansion
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* **License**: MIT
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* **Framework**: Hugging Face Transformers
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---
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## Citation
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```bibtex
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@model{josephgflowers2025phinancephi4,
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title={Phinance-Phi-4-mini-instruct-finance-v0.4-with-reasoning},
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author={Joseph G. Flowers},
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year={2025},
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url={https://huggingface.co/Josephgflowers/Phinance-Phi-4-mini-instruct-finance-v0.4-with-reasoning}
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}
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```
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