---
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*