Instructions to use QuantFactory/oxy-1-small-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/oxy-1-small-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="QuantFactory/oxy-1-small-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/oxy-1-small-GGUF", dtype="auto")

llama-cpp-python

How to use QuantFactory/oxy-1-small-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/oxy-1-small-GGUF",
	filename="oxy-1-small.Q2_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use QuantFactory/oxy-1-small-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/oxy-1-small-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/oxy-1-small-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/oxy-1-small-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/oxy-1-small-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf QuantFactory/oxy-1-small-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/oxy-1-small-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf QuantFactory/oxy-1-small-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/oxy-1-small-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/oxy-1-small-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/oxy-1-small-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/oxy-1-small-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/oxy-1-small-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/oxy-1-small-GGUF:Q4_K_M

SGLang

How to use QuantFactory/oxy-1-small-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "QuantFactory/oxy-1-small-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/oxy-1-small-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "QuantFactory/oxy-1-small-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/oxy-1-small-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use QuantFactory/oxy-1-small-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/oxy-1-small-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/oxy-1-small-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for QuantFactory/oxy-1-small-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for QuantFactory/oxy-1-small-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/oxy-1-small-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/oxy-1-small-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/oxy-1-small-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/oxy-1-small-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/oxy-1-small-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.oxy-1-small-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/oxy-1-small-GGUF

This is quantized version of oxyapi/oxy-1-small created using llama.cpp

Original Model Card

Introduction

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}},
}

Downloads last month: 127

GGUF

Model size

15B params

Architecture

qwen2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for QuantFactory/oxy-1-small-GGUF

Base model

Qwen/Qwen2.5-14B

Finetuned

Qwen/Qwen2.5-14B-Instruct

Quantized

(134)

this model