--- license: apache-2.0 base_model: - Qwen/Qwen3-30B-A3B language: - en - zh tags: - agent - sales - e-commerce - sft - dpo library_name: transformers pipeline_tag: text-generation --- # Selling-Assistant-V1
Selling Assistant Logo

Hugging Face License Python

## Introduction **Selling-Assistant-V1** is a state-of-the-art **Sales Language Model** built upon the **Qwen3-30B-A3B** architecture. Unlike complex agentic systems with separate classification modules, Selling-Assistant-V1 is an end-to-end generative model optimized via **Supervised Fine-Tuning (SFT)** and **Direct Preference Optimization (DPO)** to master the art of persuasion, negotiation, and customer service. It internalizes complex sales logic—from rapport building to closing deals—directly into its parameters, offering a streamlined, high-performance solution for e-commerce and CRM applications. ## Performances on Benchmarks We evaluate Selling-Assistant-V1 against leading general-purpose models on a proprietary **Sales Capability Benchmark**, which assesses performance across four critical dimensions: **Persuasion Rate**, **Empathy Score**, **Objection Handling**, and **Compliance**. | Benchmark (Sales Domain) | Selling-Assistant-V1 | Qwen3-30B-A3B | Llama3.2-30B-A3B-II-Dark-Champion-INSTRUCT | |--------------------------|----------------------|----------------------|----------------------| | **Persuasion Rate** | **85.4%** | 72.1% | 78.5% | | **Empathy Score (0-10)** | **9.2** | 7.8 | 8.1 | | **Objection Handling** | **88.9%** | 75.4% | 79.2% | | **Rule Compliance** | **99.1%** | 85.0% | 88.5% | | **CSAT Proxy** | **4.8/5** | 4.2/5 | 4.4/5 | ### Evaluation Parameters **Default Settings (Sales Tasks)** * Temperature: `0.7` * Top-p: `0.9` * Max new tokens: `512` * System Prompt: Standard Sales Assistant Persona For **Objection Handling** scenarios, we utilize a lower temperature (`0.5`) to ensure consistency and adherence to approved counter-arguments. ## Core Sales Techniques The model has been rigorously trained on top-tier sales methodologies, enabling it to naturally exhibit the following behaviors without external prompting: 1. **Trust Establishment & Needs Discovery** * **SPIN Questioning**: Naturally sequences Situation, Problem, Implication, and Need-payoff questions. * **Empathetic Resonance**: Validates customer emotions before proposing solutions. 2. **Value Alignment** * **FABE Framework**: Automatically translates product Features into Customer Benefits. 3. **Deal Acceleration** * **Objection Neutralization**: Addresses pricing and quality concerns with proven scripts. * **Closing Strategies**: Identifies buying signals and applies soft closes. 4. **Retention & Growth** * **Cross-Selling**: Contextually suggests relevant add-ons (Upsell/Cross-sell). ## Serve Selling-Assistant-V1 Locally For local deployment, Selling-Assistant-V1 supports high-performance inference frameworks including vLLM and SGLang. ### vLLM Install vLLM (ensure compatibility with your CUDA version): ```shell pip install -U vllm ``` Start the server: ```shell vllm serve digitalassistant-ai/Selling-Assistant-V1 \ --tensor-parallel-size 1 \ --gpu-memory-utilization 0.95 \ --max-model-len 32768 \ --served-model-name selling-assistant-v1 ``` ### SGLang Install SGLang: ```shell pip install "sglang[all]" ``` Launch the server: ```shell python3 -m sglang.launch_server \ --model-path digitalassistant-ai/Selling-Assistant-V1 \ --tp-size 1 \ --port 8000 \ --host 0.0.0.0 \ --served-model-name selling-assistant-v1 ``` ### Transformers Basic inference using Hugging Face Transformers: ```shell pip install transformers accelerate torch ``` Python Code: ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM MODEL_PATH = "digitalassistant-ai/Selling-Assistant-V1" tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH) model = AutoModelForCausalLM.from_pretrained( MODEL_PATH, device_map="auto", torch_dtype="auto" ) messages = [ {"role": "system", "content": "You are a professional sales assistant."}, {"role": "user", "content": "This phone is too expensive."} ] inputs = tokenizer.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, return_tensors="pt" ).to(model.device) outputs = model.generate( inputs, max_new_tokens=256, temperature=0.7, top_p=0.9 ) print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True)) ``` ## Future Roadmap: Agentic Architecture While V1 focuses on end-to-end generation, our next-generation **Selling-Agent-V2** will evolve into a fully autonomous system leveraging the **Model Context Protocol (MCP)**. This architecture separates cognitive reasoning from tool execution, enabling deeper integration with enterprise ecosystems.
Future Sales Agent Architecture
### Architectural Highlights 1. **Standardization of MCP Protocol** * **Unified Tool Interface**: A central **MCP Protocol Hub** standardizes interactions with external tools, allowing the agent to seamlessly query CRM data, calculate complex discounts, and access real-time competitive intelligence. * **Enterprise Integration**: Direct synchronization with sales opportunity statuses and dynamic retrieval of product parameters via FAB libraries. 2. **Advanced Agent Brain** * **Dual-Layer Memory**: Combines **Short-term Session Context** with **Long-term Customer Profiles (CDP)** to deliver hyper-personalized interactions across multiple touchpoints. * **Strategic Planning**: Implements **Sales SOP Path Planning** and **Chain-of-Thought (CoT)** reasoning to navigate complex negotiations and perform self-censorship for compliance. 3. **Modular Core Components** * The architecture re-integrates specialized modules for **Intent Recognition**, **Sentiment Analysis**, **Conversion Prediction**, and **Quality Assurance**, providing granular control and explainability over the sales process. This evolution marks the transition from *simulating* a salesperson to *deploying* an autonomous, tool-augmented sales employee. ## License This project is licensed under the [Apache 2.0 License](LICENSE). ## Citation If you use this model in your research or application, please cite: ```bibtex @misc{selling_assistant_v1, author = {Selling AI Team}, title = {Selling-Assistant-V1: A Specialized Chinese Sales Language Model}, year = {2024}, publisher = {Hugging Face}, journal = {Hugging Face Repository}, howpublished = {\url{https://huggingface.co/digitalassistant-ai/Selling-Assistant-V1}} } ```