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Bhawna2003/
ecommerce-llm-v2

Name: ecommerce-llm-v2
Availability: InStock

unsloth/Qwen2.5-7B-Instruct-bnb-4bit

finetune

apache-2.0

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📦 E-commerce Customer Support AI (Fine-tuned)

This model is a specialized version of Qwen2.5-7B-Instruct, fine-tuned using Unsloth and QLoRA for high-efficiency inference. It is designed to act as an intelligent support agent for Indian e-commerce platforms.

🚀 Key Capabilities:

Regional Logistics: Knowledge of warehouse stock status for Mumbai and Chennai hubs.
Inventory Management: Specialized in Electronics, Beauty products, and Home Appliances.
Order Tracking: Capable of guiding users through order status and cancellation flows.
Optimized Performance: 4-bit quantization allows it to run on low-resource GPUs (like T4) with high speed.

🛠️ Technical Profile:

Developer: Bhawna2003
Training Method: QLoRA (Rank 16, Alpha 32)
Framework: Unsloth (2x faster training, 60% less memory)
Primary Use Case: Customer Service Automation & Chatbot Integration.

📖 How to Use (Gradio/Python):

To run this model in a simple UI, use the following snippet in Google Colab:

python

from unsloth import FastLanguageModel
import gradio as gr

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "Bhawna2003/ecommerce-llm-v2",
    load_in_4bit = True,
)
FastLanguageModel.for_inference(model)

# Add your inference logic here...