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

Improve language tag

by lbourdois - opened Apr 28, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+111

-102

Files changed (1) hide show

README.md +111 -102

README.md CHANGED Viewed

@@ -1,102 +1,111 @@
----
-license: apache-2.0
-language:
-- en
-pipeline_tag: text-generation
-tags:
-- role-play
-- fine-tuned
-- qwen2.5
-base_model:
-- Qwen/Qwen2.5-14B-Instruct
-library_name: transformers
----
-[![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory)
-# QuantFactory/oxy-1-small-GGUF
-This is quantized version of [oxyapi/oxy-1-small](https://huggingface.co/oxyapi/oxy-1-small) created using llama.cpp
-# Original Model Card
-![Oxy 1 Small](https://cdn-uploads.huggingface.co/production/uploads/64fb80c8bb362cbf2ff96c7e/tTIVIblPUbTYnlvHQQjXB.png)
-## Introduction
-**Oxy 1 Small** is a fine-tuned version of the [Qwen/Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/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](https://huggingface.co/oxyapi/oxy-1-small)
-- **Base Model**: [Qwen/Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct)
-- **Model Type**: Chat Completions
-- **Prompt Format**: ChatML
-- **License**: Apache-2.0
-- **Language**: English
-- **Tokenizer**: [Qwen/Qwen2.5-14B-Instruct](https://huggingface.co/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](https://www.apache.org/licenses/LICENSE-2.0).
-## 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}},
-}
-```

+---
+license: apache-2.0
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+pipeline_tag: text-generation
+tags:
+- role-play
+- fine-tuned
+- qwen2.5
+base_model:
+- Qwen/Qwen2.5-14B-Instruct
+library_name: transformers
+---
+[![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory)
+# QuantFactory/oxy-1-small-GGUF
+This is quantized version of [oxyapi/oxy-1-small](https://huggingface.co/oxyapi/oxy-1-small) created using llama.cpp
+# Original Model Card
+![Oxy 1 Small](https://cdn-uploads.huggingface.co/production/uploads/64fb80c8bb362cbf2ff96c7e/tTIVIblPUbTYnlvHQQjXB.png)
+## Introduction
+**Oxy 1 Small** is a fine-tuned version of the [Qwen/Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/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](https://huggingface.co/oxyapi/oxy-1-small)
+- **Base Model**: [Qwen/Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct)
+- **Model Type**: Chat Completions
+- **Prompt Format**: ChatML
+- **License**: Apache-2.0
+- **Language**: English
+- **Tokenizer**: [Qwen/Qwen2.5-14B-Instruct](https://huggingface.co/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](https://www.apache.org/licenses/LICENSE-2.0).
+## 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}},
+}
+```