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Model: Josephgflowers/Phinance-Phi-3.5-mini-instruct-finance-v0.2 Source: Original Platform
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README.md
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---
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language:
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- en
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license: apache-2.0
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tags:
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- financial
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- fine-tuning
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- instruction-tuning
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- mini-LLM
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- finance-dataset
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- multi-turn-conversations
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- RAG
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- lightweight-finance-agent
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datasets:
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Josephgflowers/Phinance
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base_model: phi-3.5-mini-instruct
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model_type: instruct-LLM
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pipeline_tag: text-generation
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---
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# Model Card: Phinance-Phi-3.5-mini-instruct-finance-v0.2
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## Overview
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**Phinance-Phi-3.5-mini-instruct-finance-v0.2** is a fine-tuned mini language model specifically designed for financial tasks, instruction following, and multi-turn conversations. It leverages the **Phinance Dataset** to excel in finance-specific reasoning, question answering, and lightweight expert applications. The model is based on the **phi-3.5-mini** architecture, optimized for instruction-based workflows in the financial domain.
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### Key Features
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- **Finance-Focused Reasoning**: Handles complex tasks like portfolio analysis, market trends, and financial question answering.
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- **Instruction Following**: Trained for fine-grained instruction-based tasks within the financial sector.
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- **Multi-Turn Conversations**: Designed to handle context-aware dialogue with a focus on finance.
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- **RAG-Compatible**: Supports retrieval-augmented generation (RAG) through the use of data tokens (`<|data|>`) to integrate external data seamlessly.
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- **Lightweight Architecture**: Efficient for deployment on resource-constrained environments while maintaining robust performance.
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## Training Data
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The model was fine-tuned on the **Phinance Dataset**, a curated subset of financial content. The dataset includes multi-turn conversations formatted in **PHI style**, with financial relevance scored using advanced keyword matching.
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### Dataset Highlights:
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- **Topics**: Market trends, investment strategies, financial analysis, and more.
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- **Format**: Conversations in PHI format, including data tokens (`<|data|>`) for RAG use cases.
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- **Filtering**: High-quality finance-relevant content scored and selected using advanced methods.
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## Supported Tasks
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1. **Financial QA**: Answer complex questions about market analysis, financial terms, or investment strategies.
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2. **Multi-Turn Conversations**: Engage in context-aware dialogues about financial topics.
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3. **Instruction Following**: Execute finance-specific instructions and prompts with precision.
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4. **Lightweight Finance Domain Expert Agent**: Serve as an efficient, finance-focused assistant for lightweight systems.
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5. **Retrieval-Augmented Generation (RAG)**: Seamlessly integrate external data using the `<|data|>` token for enhanced responses.
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## Usage
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This model is ideal for:
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- Financial advisors or assistants
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- Chatbots and conversational agents
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- Financial QA systems
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- Lightweight domain-specific applications for finance
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### Help Here
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Like my work? Want to see more? Custom request? Message me on discord: joseph.flowers.ra Donate here: https://buymeacoffee.com/josephgflowers
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### How to Use
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "Josephgflowers/Phinance-Phi-3.5-mini-instruct-finance-v0.2"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name)
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# Example usage
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inputs = tokenizer("Explain the difference between stocks and bonds.", return_tensors="pt")
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outputs = model.generate(**inputs)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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Limitations and Considerations
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Niche Knowledge: While proficient in financial topics, the model may not perform as well on general-purpose tasks.
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Bias: Data filtering may introduce biases toward certain financial sectors or topics.
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Hallucinations: As with any language model, responses should be verified for accuracy in critical applications.
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Model Details
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Base Model: phi-3.5-mini
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Fine-Tuned Dataset: Phinance Dataset
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Version: v0.2
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Parameters: Mini-sized architecture for efficient performance
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Training Framework: Hugging Face Transformers
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License
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This model is licensed under the Apache 2.0 license.
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Citation
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If you use this model, please cite:
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@model{phinance_phi_3_5_mini_instruct_v0_2,
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title={Phinance-Phi-3.5-mini-instruct-finance-v0.2},
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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-3.5-mini-instruct-finance-v0.2}
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}
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