Wildstash/strategic-consultant-for-corporate-strategy

🤖 Strategic Consultant for Corporate Strategy (LoRA on Qwen2.5-3B)

AI-powered strategic business analyst trained with GRPO (Group Relative Policy Optimization) for expert-level business strategy and analysis.

🎯 Overview

The Strategic Consultant for Corporate Strategy is a specialized AI assistant trained on 1000+ real business strategy cases. It provides expert-level strategic analysis, actionable recommendations, and structured business insights using advanced reinforcement learning techniques.

Keywords: corporate strategy decision making, business strategy, competitive analysis, market analysis, go to market, merger and acquisition, digital transformation, business planning, organizational development, performance improvement, management consulting

✨ Key Features

• 🎯 Strategic Framework Identification: Automatically selects appropriate business frameworks
• 🔍 Root Cause Analysis: Deep analysis of business problems and opportunities
• 📋 Actionable Action Plans: Detailed plans with owners, timelines, and budgets
• 📊 Organizational Impact Assessment: Comprehensive stakeholder and resource analysis
• 🚀 Multi-Domain Expertise: Market entry, churn reduction, digital transformation, M&A

🚀 Quick Start

Use from Hugging Face (PEFT adapters)

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

base = "Qwen/Qwen2.5-3B-Instruct"
adapter = "Wildstash/strategic-consultant-for-corporate-strategy"

tokenizer = AutoTokenizer.from_pretrained(base, use_fast=True)
base_model = AutoModelForCausalLM.from_pretrained(base, torch_dtype=torch.bfloat16, device_map="auto")
model = PeftModel.from_pretrained(base_model, adapter)

prompt = "How should a startup compete against established market leaders?"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Use with Hugging Face Inference API

import requests

API_URL = "https://api-inference.huggingface.co/models/Wildstash/strategic-consultant-for-corporate-strategy"
headers = {"Authorization": "Bearer YOUR_HF_TOKEN"}

def query(payload):
    response = requests.post(API_URL, headers=headers, json=payload)
    return response.json()

output = query({
    "inputs": "A B2B SaaS company has 30% monthly churn. Recommend a strategy to reduce it to under 15%.",
    "parameters": {"max_new_tokens": 512, "temperature": 0.7}
})
### Optional: Merge LoRA → standalone checkpoint

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

base = "Qwen/Qwen2.5-3B-Instruct"
adapter = "Wildstash/strategic-consultant-for-corporate-strategy"

tok = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(base, torch_dtype=torch.bfloat16)
model = PeftModel.from_pretrained(model, adapter)
merged = model.merge_and_unload()
merged.save_pretrained("wildstash-biz-analyst-merged", safe_serialization=True)
tok.save_pretrained("wildstash-biz-analyst-merged")

## 📊 Example Output

**Input**: "B2B SaaS with 30% month-3 churn despite NPS 45. Propose a 90-day plan to reduce churn to <15%."

**Output**:

**Framework:** Systems Thinking

Root Cause Analysis: Poor customer service responsiveness and inconsistent onboarding experience

Key Stakeholders:

• Customer Service team: 15 FTEs
• Product team: 5 FTEs
• Marketing team: 8 FTEs

Organizational Impact:

• Revenue impact: $2.4M annually
• Customer lifetime value: $8,400
• Market position: Competitive disadvantage

1. **Cross-train support team** (Owner: Product Manager; Timeline: 6 weeks; Budget: $0.27M; Target: Response time <2 hours) 2. **Launch customer success program** (Owner: Marketing Director; Timeline: 5 weeks; Budget: $0.16M; Target: 25% engagement increase) 3. **Implement feedback loop system** (Owner: CTO; Timeline: 6 weeks; Budget: $0.15M; Target: 95% satisfaction score) ```

🎓 Training Details

• Base Model: Qwen/Qwen2.5-3B-Instruct (3B parameters)
• Training Method: LoRA + GRPO (Group Relative Policy Optimization)
• Dataset: Wildstash/OrgStrategy-Reasoning-1k (1000+ business strategy cases)
• Training Framework: TRL (Transformer Reinforcement Learning)
• LoRA Configuration: Rank 16, Alpha 32
• Training Duration: 2 epochs, ~4 hours on GPU
• Cost: ~$15 on AWS SageMaker

📈 Performance Metrics (self-reported)

Metric	Value
Inference Speed	1-2s per query (GPU), 30-60s (CPU)
Output Quality	Structured, actionable business strategies
Framework Coverage	15+ strategic frameworks
Domain Coverage	Market entry, churn reduction, digital transformation, M&A
Response Structure	95%+ compliance with XML format

🏗️ Architecture

┌─────────────────────────────────────────────────────┐
│                    USER INPUT                        │
│   "Help me with market entry strategy"              │
└────────────────────┬─────────────────────────────────┘
                     │
                     ▼
┌─────────────────────────────────────────────────────┐
│              Business Analyst Agent                  │
│   Qwen2.5-3B + LoRA Adapters + GRPO Training       │
└────────────────────┬─────────────────────────────────┘
                     │
                     ▼
┌─────────────────────────────────────────────────────┐
│               Structured Output                      │
│   • Strategic Analysis                              │
│   • Framework Identification                        │
│   • Action Plan with Resources                      │
│   • Impact Assessment                               │
└─────────────────────────────────────────────────────┘

🎯 Use Cases

🏢 Corporate Strategy

• Market entry strategies
• Competitive positioning
• M&A analysis and integration
• Digital transformation planning

📊 Business Analysis

• Churn reduction strategies
• Revenue optimization
• Operational efficiency
• Performance improvement

🚀 Startup Advisory

• Go-to-market strategies
• Product-market fit analysis
• Funding strategy development
• Growth planning

📈 Management Consulting

• Strategic planning
• Organizational development
• Change management
• Process optimization

🔧 Technical Specifications

• Model Size: 3B parameters (base) + 16M parameters (LoRA)
• Memory Usage: ~6GB GPU RAM (inference)
• Context Length: 32K tokens
• Output Format: Structured XML with business frameworks
• Supported Languages: English
• Deployment: Local, AWS SageMaker, HuggingFace Endpoints

📚 Dataset Information

Trained on Wildstash/OrgStrategy-Reasoning-1k, a curated dataset containing:

• 1000+ business strategy scenarios
• 15+ strategic frameworks (Systems Thinking, Lean Analytics, Blue Ocean, etc.)
• Real-world case studies from various industries
• Expert-validated responses with structured outputs
• Diverse business contexts (startups, enterprises, non-profits)

🔎 Search keywords (for discoverability)

• corporate strategy
• decision making
• business strategy
• competitive analysis
• market analysis
• go to market
• merger and acquisition
• digital transformation
• business planning
• organizational development
• performance improvement
• management consulting

🚀 Deployment Options

1. Local Inference (CPU/GPU)

pip install transformers peft torch
python -c "
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model = AutoModelForCausalLM.from_pretrained('Qwen/Qwen2.5-3B-Instruct')
model = PeftModel.from_pretrained(base_model, 'Wildstash/business-analyst-agent')
tokenizer = AutoTokenizer.from_pretrained('Qwen/Qwen2.5-3B-Instruct')
"

2. HuggingFace Inference Endpoints

• Instance: GPU Medium (~$0.60/hour)
• Setup: 5 minutes
• Scalability: Auto-scaling
• API: RESTful endpoint

3. AWS SageMaker

• Instance: ml.g5.xlarge (~$1.20/hour)
• Setup: 30 minutes
• Scalability: High
• Integration: Native AWS services

🎥 Demo Video

[Link to demo video showcasing the Business Analyst Agent]

📊 Evaluation Results (overview)

• Framework Accuracy: 92% (heuristic eval on internal set)
• Actionability: 88% (expert-judged)
• Structured Output: 95% (XML compliance)
• Business Relevance: 90%

🤝 Contributing

Contributions welcome! Open issues or PRs.

📄 License

Apache-2.0

🙏 Acknowledgments

• Base Model: Qwen2.5-3B-Instruct by Alibaba Cloud
• Training Framework: TRL by Hugging Face
• Dataset: Wildstash/OrgStrategy-Reasoning-1k
• Built for: AWS AI Agent Global Hackathon

📞 Support

• Discussions: Hugging Face Discussions

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Hugging Face: @Wildstash

Built with ❤️ for the AWS AI Agent Global Hackathon