Instructions to use SL-AI/Openprose-2-Flash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SL-AI/Openprose-2-Flash with Transformers:

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

pipe = pipeline("image-text-to-text", model="SL-AI/Openprose-2-Flash")
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("SL-AI/Openprose-2-Flash")
model = AutoModelForMultimodalLM.from_pretrained("SL-AI/Openprose-2-Flash")
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 SL-AI/Openprose-2-Flash with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SL-AI/Openprose-2-Flash"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SL-AI/Openprose-2-Flash",
		"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/SL-AI/Openprose-2-Flash

SGLang

How to use SL-AI/Openprose-2-Flash 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 "SL-AI/Openprose-2-Flash" \
    --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": "SL-AI/Openprose-2-Flash",
		"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 "SL-AI/Openprose-2-Flash" \
        --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": "SL-AI/Openprose-2-Flash",
		"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 SL-AI/Openprose-2-Flash with Docker Model Runner:
```
docker model run hf.co/SL-AI/Openprose-2-Flash
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

The Openprose 2 Flash

Model	Size	Modalities	Domain
Openprose 2 Flash	9B	Image + Text in, Text out	On-device writing

Openprose 2 Flash is a mid-sized model designed for creative writing tasks. Built on a Qwen3.5 base, it supports multimodal inputs (image + text) and was trained on 200,000 entires of human-text for creative writing.

Openprose 2 Flash does not support any capability to think.

What's New in Openprose 2

Openprose 2 Flash addresses several shortcomings from the first generation:

Stronger base model — Built on Qwen3.5 9B, a substantially more capable foundation than the Qwen3 VL model used in the original model based on Openprose.
More time spent training — With more tokens than the original model, it is capable of more complex ideas in story.
More parameters — The 9B scale places Openprose 2 Flash boosts the parameter count of Openprose 1 by 6B, making it ever more smarter

Capabilities

Openprose 2 Flash was post-trained on a curated dataset with heavy emphasis on creative writing, it will perform poorly on other domains.

GRaPE 2.1 Flash accepts image and text as input and produces text as output.

Thinking Modes

Openprose 2 Flash does not support thinking. As we move to GRaPE 2.5, we will not only advance it's writing skills, but also support thinking.

Recommended Inference Settings

Tested in LM Studio. These sampling parameters are a good starting point:

Parameter	Value
Temperature	0.6
Top K	20
Repeat Penalty	1.0
Top P	0.95
Min P	0

Notes

Openprose 2 Flash is the only Openprose 2 model that will be released.
Training data is open-source.
Updates and announcements are posted on Skinnertopia and this Hugging Face repository.

Openprose 2 Flash is developed under the SLAI (Skinnertopia Lab for Artificial Intelligence) brand and released under the Apache 2.0 license.

Downloads last month: -

Safetensors

Model size

10B params

Tensor type

BF16

F32

Model tree for SL-AI/Openprose-2-Flash

Base model

Qwen/Qwen3.5-9B-Base

Finetuned

Qwen/Qwen3.5-9B

Finetuned

SL-AI/GRaPE-2.1-Flash

Finetuned

(2)

this model

Quantizations

2 models

SL-AI
/

Openprose-2-Flash

The Openprose 2 Flash

What's New in Openprose 2

Capabilities

Thinking Modes

Recommended Inference Settings

Notes

Model tree for SL-AI/Openprose-2-Flash

Dataset used to train SL-AI/Openprose-2-Flash