Instructions to use 4bit/oxy-1-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use 4bit/oxy-1-small with Transformers:

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

pipe = pipeline("text-generation", model="4bit/oxy-1-small")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("4bit/oxy-1-small")
model = AutoModelForCausalLM.from_pretrained("4bit/oxy-1-small")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use 4bit/oxy-1-small with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "4bit/oxy-1-small"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "4bit/oxy-1-small",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/4bit/oxy-1-small

SGLang

How to use 4bit/oxy-1-small 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 "4bit/oxy-1-small" \
    --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": "4bit/oxy-1-small",
		"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 "4bit/oxy-1-small" \
        --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": "4bit/oxy-1-small",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use 4bit/oxy-1-small with Docker Model Runner:
```
docker model run hf.co/4bit/oxy-1-small
```

oxy-1-small / README.md

camenduru

thanks to oxyapi ❤

269e61a verified over 1 year ago

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	---
	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
	---

	![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/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/Qwen2-1.5B)
	- 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}},
	}
	```