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DEPLOYMENT_PLAN.md

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+# FinGPT Compliance Agents - Deployment Plan
+
+## 🎯 Overview
+
+This document outlines the deployment strategy for FinGPT Compliance Agents, including cloud deployment, model hosting, and integration options.
+
+## 📦 Model Package Contents
+
+### Core Files
+- `adapter_model.safetensors` - LoRA adapter weights
+- `adapter_config.json` - LoRA configuration
+- `tokenizer.json` - Tokenizer files
+- `tokenizer_config.json` - Tokenizer configuration
+- `special_tokens_map.json` - Special tokens mapping
+- `training_args.bin` - Training arguments
+- `README.md` - Model documentation
+
+### Supporting Files
+- `inference_example.py` - Usage examples
+- `requirements.txt` - Python dependencies
+- `config.yaml` - Model configuration
+- `evaluation_results.json` - Performance metrics
+
+## 🚀 Deployment Options
+
+### 1. Hugging Face Hub (Primary)
+
+**Status**: Ready for deployment
+**Repository**: `QXPS/fingpt-compliance-agents`
+
+#### Steps:
+1. **Create Hugging Face Repository**
+   ```bash
+   # Install huggingface_hub
+   pip install huggingface_hub
+   
+   # Login to Hugging Face
+   huggingface-cli login
+   
+   # Create repository
+   huggingface-cli repo create fingpt-compliance-agents --type model
+   ```
+
+2. **Upload Model Files**
+   ```bash
+   # Upload all model files
+   huggingface-cli upload QXPS/fingpt-compliance-agents ./models/fingpt-compliance/
+   
+   # Upload supporting files
+   huggingface-cli upload QXPS/fingpt-compliance-agents ./README.md
+   huggingface-cli upload QXPS/fingpt-compliance-agents ./requirements.txt
+   ```
+
+3. **Set Repository Settings**
+   - Make repository public
+   - Add model tags: `financial`, `compliance`, `xbrl`, `sentiment-analysis`
+   - Enable model cards and discussions
+
+### 2. Cloud Deployment
+
+#### Option A: Hugging Face Inference API
+```python
+from huggingface_hub import InferenceClient
+
+client = InferenceClient("QXPS/fingpt-compliance-agents")
+response = client.text_generation(
+    "Analyze this financial statement: ...",
+    max_new_tokens=512,
+    temperature=0.7
+)
+```
+
+#### Option B: AWS SageMaker
+```python
+# Deploy to SageMaker endpoint
+import sagemaker
+from sagemaker.huggingface import HuggingFaceModel
+
+# Create model
+huggingface_model = HuggingFaceModel(
+    model_data="s3://your-bucket/fingpt-compliance-agents",
+    role=sagemaker.get_execution_role(),
+    transformers_version="4.44.0",
+    pytorch_version="2.0.0",
+    py_version="py310"
+)
+
+# Deploy endpoint
+predictor = huggingface_model.deploy(
+    initial_instance_count=1,
+    instance_type="ml.g4dn.xlarge"
+)
+```
+
+#### Option C: Google Cloud AI Platform
+```python
+# Deploy to Google Cloud
+from google.cloud import aiplatform
+
+# Create model
+model = aiplatform.Model.upload(
+    display_name="fingpt-compliance-agents",
+    artifact_uri="gs://your-bucket/fingpt-compliance-agents",
+    serving_container_image_uri="us-docker.pkg.dev/vertex-ai/prediction/pytorch-gpu.1-12:latest"
+)
+
+# Deploy endpoint
+endpoint = model.deploy(
+    machine_type="n1-standard-4",
+    accelerator_type="NVIDIA_TESLA_T4",
+    accelerator_count=1
+)
+```
+
+### 3. Local Deployment
+
+#### Docker Container
+```dockerfile
+FROM pytorch/pytorch:2.0.0-cuda11.7-cudnn8-runtime
+
+WORKDIR /app
+COPY requirements.txt .
+RUN pip install -r requirements.txt
+
+COPY . .
+EXPOSE 8000
+
+CMD ["python", "app.py"]
+```
+
+#### FastAPI Application
+```python
+from fastapi import FastAPI
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+import torch
+
+app = FastAPI()
+
+# Load model
+base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B-Instruct")
+model = PeftModel.from_pretrained(base_model, "./models/fingpt-compliance")
+tokenizer = AutoTokenizer.from_pretrained("./models/fingpt-compliance")
+
+@app.post("/analyze")
+async def analyze_financial_text(text: str):
+    inputs = tokenizer(text, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model.generate(**inputs, max_new_tokens=512)
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return {"analysis": response}
+```
+
+## 🔧 Integration Guide
+
+### 1. Python Integration
+
+```python
+# Install dependencies
+pip install transformers peft torch
+
+# Load model
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+
+base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B-Instruct")
+model = PeftModel.from_pretrained(base_model, "QXPS/fingpt-compliance-agents")
+tokenizer = AutoTokenizer.from_pretrained("QXPS/fingpt-compliance-agents")
+
+# Use model
+def analyze_financial_text(text):
+    inputs = tokenizer(text, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model.generate(**inputs, max_new_tokens=512)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+```
+
+### 2. REST API Integration
+
+```python
+import requests
+
+# API endpoint
+url = "https://api-inference.huggingface.co/models/QXPS/fingpt-compliance-agents"
+headers = {"Authorization": f"Bearer {hf_token}"}
+
+def query_model(payload):
+    response = requests.post(url, headers=headers, json=payload)
+    return response.json()
+
+# Usage
+output = query_model({
+    "inputs": "Analyze this financial statement: Revenue increased 15%",
+    "parameters": {"max_new_tokens": 512, "temperature": 0.7}
+})
+```
+
+### 3. Streamlit Web App
+
+```python
+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+
+st.title("FinGPT Compliance Agents")
+
+# Load model
+@st.cache_resource
+def load_model():
+    base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B-Instruct")
+    model = PeftModel.from_pretrained(base_model, "QXPS/fingpt-compliance-agents")
+    tokenizer = AutoTokenizer.from_pretrained("QXPS/fingpt-compliance-agents")
+    return model, tokenizer
+
+model, tokenizer = load_model()
+
+# UI
+text_input = st.text_area("Enter financial text to analyze:")
+if st.button("Analyze"):
+    inputs = tokenizer(text_input, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model.generate(**inputs, max_new_tokens=512)
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    st.write(response)
+```
+
+## 📊 Performance Monitoring
+
+### 1. Model Metrics
+- **Inference Speed**: ~50 tokens/second
+- **Memory Usage**: ~4GB VRAM
+- **Accuracy**: 55.6% overall, 88.3% XBRL tasks
+- **Latency**: <2 seconds for 512 tokens
+
+### 2. Monitoring Setup
+```python
+import time
+import psutil
+import torch
+
+class ModelMonitor:
+    def __init__(self, model, tokenizer):
+        self.model = model
+        self.tokenizer = tokenizer
+        self.metrics = []
+    
+    def log_inference(self, input_text, output_text, inference_time):
+        self.metrics.append({
+            "timestamp": time.time(),
+            "input_length": len(input_text),
+            "output_length": len(output_text),
+            "inference_time": inference_time,
+            "memory_usage": psutil.virtual_memory().percent,
+            "gpu_memory": torch.cuda.memory_allocated() if torch.cuda.is_available() else 0
+        })
+```
+
+## 🔒 Security Considerations
+
+### 1. API Security
+- **Authentication**: Use Hugging Face tokens or API keys
+- **Rate Limiting**: Implement request throttling
+- **Input Validation**: Sanitize user inputs
+- **Output Filtering**: Remove sensitive information
+
+### 2. Model Security
+- **Model Integrity**: Verify model weights and configuration
+- **Data Privacy**: Ensure no sensitive data in training
+- **Access Control**: Limit model access to authorized users
+
+## 📈 Scaling Strategy
+
+### 1. Horizontal Scaling
+- **Load Balancing**: Distribute requests across multiple instances
+- **Auto-scaling**: Scale based on demand
+- **Caching**: Cache frequent requests
+
+### 2. Vertical Scaling
+- **GPU Optimization**: Use larger GPUs for better performance
+- **Memory Optimization**: Implement model quantization
+- **Batch Processing**: Process multiple requests together
+
+## 🧪 Testing Strategy
+
+### 1. Unit Tests
+```python
+import unittest
+from your_model import FinGPTCompliance
+
+class TestFinGPTCompliance(unittest.TestCase):
+    def setUp(self):
+        self.model = FinGPTCompliance()
+    
+    def test_financial_qa(self):
+        result = self.model.answer_question("What is revenue?")
+        self.assertIsInstance(result, str)
+        self.assertGreater(len(result), 0)
+    
+    def test_sentiment_analysis(self):
+        result = self.model.analyze_sentiment("Stock price increased")
+        self.assertIn(result, ["positive", "negative", "neutral"])
+```
+
+### 2. Integration Tests
+```python
+def test_api_integration():
+    response = requests.post("/api/analyze", json={"text": "Test"})
+    assert response.status_code == 200
+    assert "analysis" in response.json()
+```
+
+### 3. Performance Tests
+```python
+def test_performance():
+    start_time = time.time()
+    result = model.analyze("Test text")
+    inference_time = time.time() - start_time
+    assert inference_time < 2.0  # Should complete within 2 seconds
+```
+
+## 📋 Deployment Checklist
+
+### Pre-deployment
+- [ ] Model weights verified and tested
+- [ ] Documentation updated
+- [ ] Performance benchmarks completed
+- [ ] Security review passed
+- [ ] Integration tests passed
+
+### Deployment
+- [ ] Repository created on Hugging Face
+- [ ] Model files uploaded
+- [ ] Model card published
+- [ ] API endpoints configured
+- [ ] Monitoring setup
+
+### Post-deployment
+- [ ] Smoke tests passed
+- [ ] Performance monitoring active
+- [ ] User feedback collected
+- [ ] Documentation updated
+- [ ] Support channels established
+
+## 🚀 Next Steps
+
+1. **Immediate**: Upload model to Hugging Face Hub
+2. **Short-term**: Set up monitoring and basic API
+3. **Medium-term**: Implement advanced features and scaling
+4. **Long-term**: Continuous improvement and expansion
+
+## 📞 Support
+
+- **GitHub Issues**: [Repository Issues](https://github.com/your-repo/fingpt-compliance-agents/issues)
+- **Hugging Face**: [Model Discussion](https://huggingface.co/QXPS/fingpt-compliance-agents/discussions)
+- **Email**: support@your-domain.com
+
+---
+
+**Last Updated**: January 2025
+**Version**: 1.0.0
+**Status**: Ready for Deployment