dheerajdasari/Customer-support-instruct-1B

Ansah-E1

This repository contains a fully merged, 4-bit quantized model built by integrating a customer support adapter into the base Llama-3.2-1B-Instruct model.

Model Overview

• Base Model: Llama-3.2-1B-Instruct from Meta
• Adapter: Customer Support Chatbot fine-tuned for customer support scenarios
• Merged Model: The adapter weights have been fully merged into the base model for streamlined inference
• Quantization: The model is quantized to 4-bit for improved efficiency while maintaining performance

Usage

This model behaves like any other Hugging Face model. For example:

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("your_username/Ansah-E1", load_in_4bit=True)
tokenizer = AutoTokenizer.from_pretrained("your_username/Ansah-E1")

prompt = "I received a damaged product and want to return it. What's the process?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))