Update README.md

README.md

@@ -1,3 +1,201 @@
----
-license: apache-2.0
----
+---
+license: mit
+base_model: microsoft/Phi-3-mini-4k-instruct
+tags:
+- phi3
+- finance
+- financial-qa
+- fintech
+- fine-tuned
+- lora
+- 4bit
+language:
+- en
+library_name: transformers
+pipeline_tag: text-generation
+datasets:
+- FinGPT/fingpt-fiqa_qa
+---
+
+# Phi-3-Mini FinSight Financial Q&A Assistant
+
+This model is a fine-tuned version of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) specialized for financial question answering. It serves as the core reasoning engine for the FinSight 360 financial intelligence system.
+
+## Model Details
+
+### Model Description
+
+A 3.8B parameter language model fine-tuned using LoRA (Low-Rank Adaptation) on financial Q&A data. The model is optimized to answer questions about investments, banking, personal finance, and corporate finance topics.
+
+- **Developed by:** sweatSmile
+- **Model type:** Causal Language Model (Decoder-only Transformer)
+- **Language(s):** English
+- **License:** MIT
+- **Finetuned from model:** microsoft/Phi-3-mini-4k-instruct
+
+### Model Sources
+
+- **Repository:** [More Information Needed]
+- **Base Model:** [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct)
+
+## Uses
+
+### Direct Use
+
+This model can be used directly for:
+- Answering financial and investment questions
+- Explaining financial concepts and terminology
+- Providing guidance on personal finance topics
+- Educational purposes for financial literacy
+
+### Downstream Use
+
+The model is designed as a component of the FinSight 360 system, which includes:
+- Real-time financial data retrieval (RAG architecture)
+- Risk assessment and sentiment analysis
+- Entity extraction from financial documents
+- Interactive financial dashboard
+
+### Out-of-Scope Use
+
+- **Not financial advice:** This model is for educational and informational purposes only
+- **Not for trading decisions:** Should not be used as sole basis for investment decisions
+- **Not licensed advice:** Does not replace consultation with qualified financial advisors
+- **Not for emergency financial situations:** Cannot provide real-time crisis management
+
+## Bias, Risks, and Limitations
+
+- Trained on only 500 samples - limited coverage of specialized financial topics
+- May not reflect the most current market conditions or regulations
+- Potential bias toward certain financial instruments or strategies present in training data
+- Cannot access real-time market data or perform live calculations
+- May generate plausible-sounding but incorrect information (hallucinations)
+
+### Recommendations
+
+Users should:
+- Verify all financial information with qualified professionals
+- Not use for actual investment or financial decisions without expert consultation
+- Be aware of the model's training data limitations
+- Cross-reference answers with authoritative financial sources
+
+## How to Get Started with the Model
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "sweatSmile/Phi3-Mini-FinSight-FinancialQA"
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
+
+prompt = """<|system|>
+You are FinSight, an expert financial advisor.<|end|>
+<|user|>
+What's the difference between a Roth IRA and a Traditional IRA?<|end|>
+<|assistant|>
+"""
+
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.7, do_sample=True)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```
+
+## Training Details
+
+### Training Data
+
+Fine-tuned on 500 samples from [FinGPT/fingpt-fiqa_qa](https://huggingface.co/datasets/FinGPT/fingpt-fiqa_qa), which contains financial questions and expert answers covering:
+- Investment strategies
+- Banking and credit
+- Personal finance management
+- Corporate finance concepts
+- Market analysis
+
+### Training Procedure
+
+#### Training Hyperparameters
+
+- **Training regime:** bf16 mixed precision
+- **Epochs:** 3
+- **Learning rate:** 2e-5
+- **Batch size:** 4 per device
+- **Max sequence length:** 1024 tokens
+- **Optimizer:** AdamW with cosine learning rate schedule
+- **Warmup ratio:** 0.1
+- **Weight decay:** 0.01
+- **Gradient clipping:** 1.0
+
+#### LoRA Configuration
+
+- **Rank (r):** 8
+- **Alpha:** 16
+- **Dropout:** 0.1
+- **Target modules:** All linear layers
+- **Quantization:** 4-bit (nf4)
+
+#### Speeds, Sizes, Times
+
+- **Training time:** ~30 minutes on single T4 GPU
+- **Model size (merged):** ~7GB
+- **Hardware:** Google Colab T4 GPU (16GB VRAM)
+
+## Evaluation
+
+Due to the small training set (500 samples), formal evaluation metrics were not computed. The model is intended as a proof-of-concept and component for the larger FinSight 360 system.
+
+### Example Outputs
+
+**Question:** "How do interest rates affect stock prices?"  
+**Response:** [Model provides explanation of inverse relationship between rates and equity valuations]
+
+**Question:** "What is diversification in investing?"  
+**Response:** [Model explains portfolio risk management through asset allocation]
+
+## Technical Specifications
+
+### Model Architecture and Objective
+
+- **Architecture:** Phi-3 (Dense transformer decoder)
+- **Parameters:** 3.8 billion (base model)
+- **Trainable parameters:** ~4.2 million via LoRA (0.11% of base)
+- **Context window:** 4,096 tokens
+- **Objective:** Causal language modeling with cross-entropy loss
+
+### Compute Infrastructure
+
+#### Hardware
+
+- GPU: NVIDIA T4 (16GB VRAM)
+- Platform: Google Colab
+
+#### Software
+
+- Transformers: 4.x
+- TRL (Transformer Reinforcement Learning)
+- PEFT (Parameter-Efficient Fine-Tuning)
+- PyTorch: 2.x
+- bitsandbytes (4-bit quantization)
+
+## Citation
+
+**BibTeX:**
+
+```bibtex
+@misc{phi3-finsight-2025,
+  author = {sweatSmile},
+  title = {Phi-3-Mini FinSight Financial Q&A Assistant},
+  year = {2025},
+  publisher = {HuggingFace},
+  journal = {HuggingFace Model Hub},
+  howpublished = {\url{https://huggingface.co/sweatSmile/Phi3-Mini-FinSight-FinancialQA}}
+}
+```
+
+## Model Card Authors
+
+sweatSmile
+
+## Model Card Contact
+
+Available via HuggingFace profile