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temperaturetop_ptop_kfrequency_penaltypresence_penaltymax_tokens1from transformers import AutoModelForCausalLM, AutoTokenizer
2
3tokenizer = AutoTokenizer.from_pretrained("oxyapi/oxy-1-small")
4model = AutoModelForCausalLM.from_pretrained("oxyapi/oxy-1-small")
5
6prompt = "You are a wise old wizard in a mystical land. A traveler approaches you seeking advice."
7inputs = tokenizer(prompt, return_tensors="pt")
8outputs = model.generate(**inputs, max_length=500)
9response = tokenizer.decode(outputs[0], skip_special_tokens=True)
10print(response)@misc{oxy1small2024,
title={Oxy 1 Small: A Fine-Tuned Qwen2.5-14B-Instruct Model for Role-Play},
author={Oxygen (oxyapi)},
year={2024},
howpublished={\url{https://huggingface.co/oxyapi/oxy-1-small}},
}