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---
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">
</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* |