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.

Model Details

Model Name: Oxy 1 Small
Model ID: oxyapi/oxy-1-small
Base Model: Qwen/Qwen2.5-14B-Instruct
Model Type: Chat Completions
Prompt Format: ChatML
License: Apache-2.0
Language: English
Tokenizer: Qwen/Qwen2.5-14B-Instruct
Max Input Tokens: 32,768
Max Output Tokens: 8,192

Features

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

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("oxyapi/oxy-1-small")
model = AutoModelForCausalLM.from_pretrained("oxyapi/oxy-1-small")

prompt = "You are a wise old wizard in a mystical land. A traveler approaches you seeking advice."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=500)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Performance

Performance benchmarks for Oxy 1 Small are not available at this time. Future updates may include detailed evaluations on relevant datasets.

License

This model is licensed under the Apache 2.0 License.

Citation

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}},
}

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	33.14
IFEval (0-Shot)	62.45
BBH (3-Shot)	41.18
MATH Lvl 5 (4-Shot)	18.28
GPQA (0-shot)	16.22
MuSR (0-shot)	16.28
MMLU-PRO (5-shot)	44.45

lucyknada/oxyapi_oxy-1-small-exl2

exl2 quant (measurement.json in main branch)

check revisions for quants

Introduction