---
license: agpl-3.0
datasets:
- bitext/Bitext-customer-support-llm-chatbot-training-dataset
language:
- en
base_model:
- meta-llama/Llama-3.2-1B-Instruct
---
# 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](https://huggingface.co/meta-llama/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))
```