Instructions to use Vortex5/G4-Starry-Ocean-12B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Vortex5/G4-Starry-Ocean-12B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="Vortex5/G4-Starry-Ocean-12B") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("Vortex5/G4-Starry-Ocean-12B") model = AutoModelForMultimodalLM.from_pretrained("Vortex5/G4-Starry-Ocean-12B", device_map="auto") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Vortex5/G4-Starry-Ocean-12B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Vortex5/G4-Starry-Ocean-12B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Vortex5/G4-Starry-Ocean-12B", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/Vortex5/G4-Starry-Ocean-12B
- SGLang
How to use Vortex5/G4-Starry-Ocean-12B 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 "Vortex5/G4-Starry-Ocean-12B" \ --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": "Vortex5/G4-Starry-Ocean-12B", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'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 "Vortex5/G4-Starry-Ocean-12B" \ --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": "Vortex5/G4-Starry-Ocean-12B", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Docker Model Runner
How to use Vortex5/G4-Starry-Ocean-12B with Docker Model Runner:
docker model run hf.co/Vortex5/G4-Starry-Ocean-12B
Sampler Question
Sampler choice used to be highly opinionated but I haven't seen a lot of people listing what parameters they think work best with gemma-4. I eventually found the generator config in the base model that said temp 1, top_k 64, and top_p 0.95 but usually those values are more on the conservative side and not designed for roleplay. Do you have any preferred samplers you use or recommend?
Sampler choice used to be highly opinionated but I haven't seen a lot of people listing what parameters they think work best with gemma-4. I eventually found the generator config in the base model that said temp 1, top_k 64, and top_p 0.95 but usually those values are more on the conservative side and not designed for roleplay. Do you have any preferred samplers you use or recommend?
Defaults should be fine for roleplay. I’m unsure if there’s a perfect combination, so you can tinker around and find what you enjoy.
Try not to raise the temperature too high, as this can cause incoherence.
Early tests on other models the Temperature going above 0.9 i was having wonky results (though that was a lot of 4B-8B models typically Q8_0); While today temperature between 0.75-0.85 seems quite good. Repetition penalty of 0.15 to avoid getting stuck, and that's about it for me. Otherwise defaults tend to work pretty well.
I also got a logit bias against certain words being used, especially against ozone...
Though to be honest, the Gemma4 series since it came out has been quite good, and it's MOE nature (26B) also makes it very fast. I do enjoy the 70B outputs (that i was using before) but it's really hard to argue with a 10x speedup that's competent; And while the Gemma4 12B and 31B models just chug... they might be good too, but tend to be too slow for me at present.
Wish the hardware thing will resolve itself price-wise but it looks like it may be too expensive to get something else til after 2030... Or if another company comes in selling bulk cheap ram and GPU's that are worth perusing....