metadata
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 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))