nvidia/Llama-Nemotron-Post-Training-Dataset
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How to use MuzammilKhosa/opalhours-ai with Adapters:
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("undefined")
model.load_adapter("MuzammilKhosa/opalhours-ai", set_active=True)OpalHours AI is a lightweight language model designed to assist with watch-related e-commerce content. It helps generate product descriptions, respond to customer queries, and maintain a consistent brand tone—especially for businesses communicating in both English and Roman Urdu.
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("opalhours/opalhours-ai")
model = AutoModelForCausalLM.from_pretrained("opalhours/opalhours-ai")
prompt = "Describe a minimal silver dial men's watch with a black leather strap."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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