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-test
+<h2>Usage</h2>
+
+<p>You can load models using the Hugging Face Transformers library:</p>
+
+<p style="background-color: gray">
+from transformers import pipeline
+
+pipe = pipeline("text-generation", model="nroggendorff/mayo")
+
+question = "What color is the sky?"
+conv = [{"role": "user", "content": question}]
+
+response = pipe(conv, max_new_tokens=32)[0]['generated_text'][-1]['content']
+print(response)
+</p>
+
+<p>To use models with quantization:</p>
+
+<p style="background-color: gray">
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+import torch
+
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.bfloat16
+)
+
+model_id = "nroggendorff/mayo"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config)
+
+question = "What color is the sky?"
+prompt = tokenizer.apply_chat_template([{"role": "user", "content": question}], tokenize=False)
+inputs = tokenizer(prompt, return_tensors="pt")
+
+outputs = model.generate(**inputs, max_new_tokens=32)
+
+generated_text = tokenizer.batch_decode(outputs)[0]
+print(generated_text)
+</p>