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.
Boost your existing LLM's CRM capabilities by using ARC Advisor as a preprocessing step:
1from transformers import AutoModelForCausalLM, AutoTokenizer
2
3# Load ARC Advisor
4advisor = AutoModelForCausalLM.from_pretrained("aman-jaglan/arc-advisor")
5tokenizer = AutoTokenizer.from_pretrained("aman-jaglan/arc-advisor")
6
7def enhance_llm_query(user_request):
8 # Step 1: Get advisory guidance
9 advisor_prompt = f"""As a CRM expert, provide guidance for this request:
10 {user_request}
11
12 Suggest the best approach, relevant objects, and query structure."""
13
14 inputs = tokenizer(advisor_prompt, return_tensors="pt")
15 advice = advisor.generate(**inputs, max_new_tokens=200)
16
17 # Step 2: Use advice to enhance main LLM prompt
18 enhanced_prompt = f"""
19 Expert Guidance: {tokenizer.decode(advice[0])}
20
21 Now execute: {user_request}
22 """
23
24 return enhanced_prompt
1import openai
2
3# Get advisor guidance first
4advice = get_arc_advisor_guidance(query)
5
6# Enhanced GPT query
7response = openai.ChatCompletion.create(
8 model="gpt-4",
9 messages=[
10 {"role": "system", "content": f"CRM Expert Guidance: {advice}"},
11 {"role": "user", "content": original_query}
12 ]
13)
1# Deploy ARC Advisor on lightweight infrastructure
2# Use output to guide larger local models
3advisor_server = "http://localhost:8000/v1/chat/completions"
4main_llm_server = "http://localhost:8001/v1/chat/completions"
1# Using Transformers
2from transformers import pipeline
3advisor = pipeline("text-generation", model="aman-jaglan/arc-advisor")
4
5# Using vLLM (recommended for production)
6python -m vllm.entrypoints.openai.api_server \
7 --model aman-jaglan/arc-advisor \
8 --dtype bfloat16 \
9 --max-model-len 4096
