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README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- en
+library_name: transformers
+tags:
+- advisory
+- llm-enhancement
+- crm
+- salesforce
+- decision-support
+base_model: Qwen/Qwen3-4B
+---
+
+# ARC Advisor: Intelligent CRM Query Assistant for LLMs
+
+<div align="center">
+
+![Model Architecture](https://img.shields.io/badge/Architecture-Advisory%20AI-blue)
+![Performance](https://img.shields.io/badge/LLM%20Improvement-X%25-green)
+![License](https://img.shields.io/badge/License-Apache%202.0-yellow)
+
+</div>
+
+## 🚀 Model Overview
+
+ARC Advisor is a specialized advisory model designed to enhance Large Language Models' performance on CRM and Salesforce-related tasks. By providing intelligent guidance and query structuring suggestions, it helps LLMs achieve significantly better results on complex CRM operations.
+
+### ✨ Key Benefits
+
+- **X% Performance Boost**: Improves LLM accuracy on CRM tasks when used as an advisor
+- **Intelligent Query Planning**: Provides structured approaches for complex Salesforce queries
+- **Error Prevention**: Identifies potential pitfalls before query execution
+- **Cost Efficient**: Small 4B model provides guidance to larger models, reducing overall compute costs
+
+## 🎯 Use Cases
+
+### 1. LLM Performance Enhancement
+Boost your existing LLM's CRM capabilities by using ARC Advisor as a preprocessing step:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load ARC Advisor
+advisor = AutoModelForCausalLM.from_pretrained("aman-jaglan/arc-advisor")
+tokenizer = AutoTokenizer.from_pretrained("aman-jaglan/arc-advisor")
+
+def enhance_llm_query(user_request):
+    # Step 1: Get advisory guidance
+    advisor_prompt = f"""As a CRM expert, provide guidance for this request:
+    {user_request}
+    
+    Suggest the best approach, relevant objects, and query structure."""
+    
+    inputs = tokenizer(advisor_prompt, return_tensors="pt")
+    advice = advisor.generate(**inputs, max_new_tokens=200)
+    
+    # Step 2: Use advice to enhance main LLM prompt
+    enhanced_prompt = f"""
+    Expert Guidance: {tokenizer.decode(advice[0])}
+    
+    Now execute: {user_request}
+    """
+    
+    return enhanced_prompt
+```
+
+### 2. Query Optimization
+Transform vague requests into structured CRM queries:
+
+- **Input**: "Show me our best customers from last quarter"
+- **ARC Advisor Output**: Structured approach with relevant Salesforce objects, filters, and aggregations
+- **Result**: Precise SOQL query with proper date ranges and metrics
+
+### 3. Multi-Step Reasoning
+Guide LLMs through complex multi-object queries:
+
+- Lead-to-Opportunity conversion analysis
+- Cross-object relationship queries
+- Time-based trend analysis
+- Performance metric calculations
+
+## 🛠️ Integration Examples
+
+### With OpenAI GPT Models
+```python
+import openai
+
+# Get advisor guidance first
+advice = get_arc_advisor_guidance(query)
+
+# Enhanced GPT query
+response = openai.ChatCompletion.create(
+    model="gpt-4",
+    messages=[
+        {"role": "system", "content": f"CRM Expert Guidance: {advice}"},
+        {"role": "user", "content": original_query}
+    ]
+)
+```
+
+### With Local LLMs (vLLM)
+```python
+# Deploy ARC Advisor on lightweight infrastructure
+# Use output to guide larger local models
+advisor_server = "http://localhost:8000/v1/chat/completions"
+main_llm_server = "http://localhost:8001/v1/chat/completions"
+```
+
+## 📊 Performance Impact
+
+When used as an advisor:
+- **Query Success Rate**: +X% improvement
+- **Complex Query Handling**: +X% accuracy boost
+- **Error Reduction**: X% fewer malformed queries
+- **Time to Solution**: X% faster query resolution
+
+## 🔧 Deployment
+
+### Quick Start
+```bash
+# Using Transformers
+from transformers import pipeline
+advisor = pipeline("text-generation", model="aman-jaglan/arc-advisor")
+
+# Using vLLM (recommended for production)
+python -m vllm.entrypoints.openai.api_server \
+    --model aman-jaglan/arc-advisor \
+    --dtype bfloat16 \
+    --max-model-len 4096
+```
+
+### Resource Requirements
+- **GPU Memory**: 8GB (bfloat16)
+- **CPU**: Supported with reduced speed
+- **Optimal Batch Size**: 32-64 requests
+
+## 🏆 Why ARC Advisor?
+
+1. **Specialized Expertise**: Trained specifically for CRM/Salesforce domain
+2. **Efficient Architecture**: Small model that enhances larger models
+3. **Production Ready**: Optimized for low-latency advisory generation
+4. **Cost Effective**: Reduce expensive LLM calls through better query planning
+
+## 📚 Model Details
+
+- **Architecture**: Qwen3-4B base with specialized fine-tuning
+- **Context Length**: 4096 tokens
+- **Output Format**: Structured advisory guidance
+- **Language**: English
+
+## 🤝 Community
+
+Join our community to share your experiences and improvements:
+- Report issues on the [model repository](https://huggingface.co/aman-jaglan/arc-advisor)
+- Share your integration examples
+- Contribute to best practices documentation
+
+## 📜 License
+
+Apache 2.0 - Commercial use permitted with attribution
+
+---
+
+*Transform your LLM into a CRM expert with ARC Advisor*