Oxy 1 Small is a fine-tuned version of the Qwen/Qwen2.5-14B-Instruct language model, specialized for role-play scenarios. Despite its small size, it delivers impressive performance in generating engaging dialogues and interactive storytelling.
Developed by Oxygen (oxyapi), with contributions from TornadoSoftwares, Oxy 1 Small aims to provide an accessible and efficient language model for creative and immersive role-play experiences.
Fine-tuned for Role-Play: Specially trained to generate dynamic and contextually rich role-play dialogues.
Efficient: Compact model size allows for faster inference and reduced computational resources.
Parameter Support:
temperature
top_p
top_k
frequency_penalty
presence_penalty
max_tokens
Metadata
Owned by: Oxygen (oxyapi)
Contributors: TornadoSoftwares
Description: A Qwen/Qwen2.5-14B-Instruct fine-tune for role-play trained on custom datasets
Usage
To utilize Oxy 1 Small for text generation in role-play scenarios, you can load the model using the Hugging Face Transformers library:
python
1from transformers import AutoModelForCausalLM, AutoTokenizer
23tokenizer = AutoTokenizer.from_pretrained("oxyapi/oxy-1-small")4model = AutoModelForCausalLM.from_pretrained("oxyapi/oxy-1-small")56prompt ="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)
Performance
Performance benchmarks for Oxy 1 Small are not available at this time. Future updates may include detailed evaluations on relevant datasets.
If you find Oxy 1 Small useful in your research or applications, please cite it as:
@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}},
}
