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