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-<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>