Instructions to use OpceanAI/Yuuki-RxG-vl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpceanAI/Yuuki-RxG-vl with Transformers:

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

pipe = pipeline("image-text-to-text", model="OpceanAI/Yuuki-RxG-vl")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("OpceanAI/Yuuki-RxG-vl", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use OpceanAI/Yuuki-RxG-vl with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpceanAI/Yuuki-RxG-vl"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpceanAI/Yuuki-RxG-vl",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/OpceanAI/Yuuki-RxG-vl

SGLang

How to use OpceanAI/Yuuki-RxG-vl 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 "OpceanAI/Yuuki-RxG-vl" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpceanAI/Yuuki-RxG-vl",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "OpceanAI/Yuuki-RxG-vl" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpceanAI/Yuuki-RxG-vl",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Studio new

How to use OpceanAI/Yuuki-RxG-vl 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 OpceanAI/Yuuki-RxG-vl 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 OpceanAI/Yuuki-RxG-vl to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for OpceanAI/Yuuki-RxG-vl to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="OpceanAI/Yuuki-RxG-vl",
    max_seq_length=2048,
)

Docker Model Runner
How to use OpceanAI/Yuuki-RxG-vl with Docker Model Runner:
```
docker model run hf.co/OpceanAI/Yuuki-RxG-vl
```

OpceanAI commited on 25 days ago

Commit

7caa87c

verified ·

1 Parent(s): 2061ee7

Update README.md

Browse files

Files changed (1) hide show

README.md +28 -3

README.md CHANGED Viewed

@@ -1,3 +1,28 @@
----
-license: apache-2.0
----

+---
+license: apache-2.0
+base_model:
+- Qwen/Qwen3.6-27B
+datasets:
+- OpceanAI/Yuuki-Personality
+- ianncity/KIMI-K2.5-1000000x
+- Roman1111111/claude-opus-4.6-10000x
+- open-r1/OpenR1-Math-220k
+language:
+- en
+- es
+library_name: transformers
+tags:
+- reasoning
+- unsloth
+- pytorch
+- bilingual
+- opceanai
+- yuuki
+- rxg
+- 27b
+- fine-tuned
+- chat
+- deepseek
+- qwen3.6
+pipeline_tag: image-text-to-text
+---