metadata
license: mit
tags:
- Jerome Powell AI model
- Federal Reserve chatbot
- fine-tuned Phi-3
- financial language model
- LLM fine-tuning
- machine learning engineering
- LoRA training
- NLP
datasets:
- BoostedJonP/JeromePowell-SFT
language:
- en
base_model:
- microsoft/Phi-3-mini-4k-instruct
pipeline_tag: text-generation
Powell-Phi3-Mini — Jerome Powell Style Language Model
🎯Summary
Powell-Phi3-Mini is an fine-tuned language model that replicates Federal Reserve Chair Jerome Powell's distinctive communication style, tone, and strategic hedging patterns. This project showcases expertise in modern LLM fine-tuning techniques, parameter-efficient training methods, and responsible AI development — demonstrating industry-ready machine learning engineering skills.
🚀 Key Features & Capabilities
Style Mimicry & Linguistic Analysis
- ✅ Authentic Communication Style: Replicates Powell's cautious, data-dependent rhetoric
- ✅ Strategic Hedging Patterns: Maintains appropriate uncertainty in speculative scenarios
- ✅ Domain-Specific Responses: Handles economic and monetary policy discussions contextually
- ✅ Refusal Training: Appropriately declines to provide financial advice or policy predictions (to an extent)
Technical Implementation
- ✅ Efficient Architecture: Built on Microsoft Phi-3-mini-4k-instruct (3.8B parameters)
- ✅ Scalable Training: LoRA r=16, alpha=32 configuration optimized for consumer GPUs
- ✅ Deployment Flexibility: Available as lightweight adapter or full merged model
- ✅ Integration Ready: One-line inference with Hugging Face Transformers
💻 Implementation Examples
Production Ready - Merged Model
from transformers import AutoTokenizer, AutoModelForCausalLM
# One-line model loading
tokenizer = AutoTokenizer.from_pretrained("BoostedJonP/powell-phi3-mini")
model = AutoModelForCausalLM.from_pretrained("BoostedJonP/powell-phi3-mini", device_map="auto")
# Economic analysis prompt
prompt = "How is the current labor market affecting your inflation outlook?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
response = model.generate(**inputs, max_new_tokens=200, do_sample=True)
print(tokenizer.decode(response[0], skip_special_tokens=True))
📊 Technical Specifications & Training Pipeline
Model Architecture
| Component | Specification |
|---|---|
| Base Model | microsoft/Phi-3-mini-4k-instruct (3.8B parameters) |
| License | MIT License (Commercial Use Approved) |
| Fine-tuning Method | QLoRA with PEFT integration |
| Context Length | 4,096 tokens |
| Training Hardware | NVIDIA TESLA P100 (16GB VRAM) |
Training Configuration
| Hyperparameter | Value | Rationale |
|---|---|---|
| LoRA Rank (r) | 16 | Optimal parameter/performance balance |
| LoRA Alpha | 32 | 2x rank for stable training |
| Dropout Rate | 0.05 | Regularization without overfitting |
| Learning Rate | 1.5e-4 | Conservative rate for stable convergence |
| Scheduler | Cosine decay | Smooth learning rate reduction |
| Training Epochs | 3 | Prevents overfitting on specialized domain |
| Sequence Length | 1,536 tokens | Optimized for dataset |
| Precision | Mixed fp16 | 2x memory efficiency, maintained accuracy |
Dataset & Methodology
- Data Source: Public domain FOMC transcripts and Federal Reserve speeches -> Jerome Powell Press Release Q&A
- Data Processing: Instruction-response pairs extracted from press conferences -> Jerome Powell Press Release SFT data processing
- Available on HuggingFace
- Quality Control: Manual review and filtering for authentic Powell communication patterns
📈 Performance Metrics & Evaluation
Quantitative Results
| Metric | Baseline (Phi-3) | Powell-Phi3-Mini | Improvement |
|---|---|---|---|
| Powell-style Classification | NA | NA | NA |
| Economic Domain Accuracy | NA | NA | NA |
| Response Coherence (BLEU) | NA | NA | NA |
Qualitative Assessment
- NA
🌐 Deployment & Access
🚀 Live Demo
Try Powell-Phi3-Mini Interactive Demo →
📦 Model Downloads
- Adapter Version:
BoostedJonP/powell-phi3-mini-adapter - Merged Model:
BoostedJonP/powell-phi3-mini(Full Model - 7.4GB)
🔗 Resources
- GitHub Repository: Complete training code and evaluation scripts
- Technical Blog Post: Detailed implementation walkthrough
- Hugging Face Collection: All model variants and datasets
⚖️ Responsible AI & Legal Compliance
Ethical Considerations
- ⚠️ No Official Affiliation: Not endorsed by or affiliated with the Federal Reserve System
- ⚠️ Educational Purpose Only: Designed for research, education, and demonstration purposes
- ⚠️ No Financial Advice: Model responses should not be interpreted as investment guidance
- ⚠️ Transparency: All training data sourced from public domain government transcripts
Licensing & Usage Rights
- Base Model License: MIT License (Microsoft Phi-3)
- Fine-tuned Weights: MIT License (Commercial use permitted)
- Training Data: Public domain (U.S. government works)
- Usage: Unrestricted for research, education, and commercial applications
👨💻 Connect & Collaborate
- GitHub: Jonathan Paserman
- Kaggle: Jonathan Paserman
- HuggingFace: Jonathan Paserman