Triangle104/oxy-1-small-Q4_K_S-GGUF

This model was converted to GGUF format from oxyapi/oxy-1-small using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

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Model details:

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:

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}}, }

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Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo Triangle104/oxy-1-small-Q4_K_S-GGUF --hf-file oxy-1-small-q4_k_s.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/oxy-1-small-Q4_K_S-GGUF --hf-file oxy-1-small-q4_k_s.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo Triangle104/oxy-1-small-Q4_K_S-GGUF --hf-file oxy-1-small-q4_k_s.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/oxy-1-small-Q4_K_S-GGUF --hf-file oxy-1-small-q4_k_s.gguf -c 2048