Instructions to use websfactory/Webs-Ko-27B-v0-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use websfactory/Webs-Ko-27B-v0-test with Transformers:

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

pipe = pipeline("image-text-to-text", model="websfactory/Webs-Ko-27B-v0-test")
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("websfactory/Webs-Ko-27B-v0-test")
model = AutoModelForMultimodalLM.from_pretrained("websfactory/Webs-Ko-27B-v0-test")
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 websfactory/Webs-Ko-27B-v0-test with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "websfactory/Webs-Ko-27B-v0-test"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "websfactory/Webs-Ko-27B-v0-test",
		"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/websfactory/Webs-Ko-27B-v0-test

SGLang

How to use websfactory/Webs-Ko-27B-v0-test 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 "websfactory/Webs-Ko-27B-v0-test" \
    --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": "websfactory/Webs-Ko-27B-v0-test",
		"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 "websfactory/Webs-Ko-27B-v0-test" \
        --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": "websfactory/Webs-Ko-27B-v0-test",
		"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 websfactory/Webs-Ko-27B-v0-test with Docker Model Runner:
```
docker model run hf.co/websfactory/Webs-Ko-27B-v0-test
```

Webs-Ko-27B-v0-test

Test / pipeline-validation merge by 웹스팩토리 (websfactory).

Single-donor DARE-TIES merge of Qwen/Qwen3.6-27B (base) + dnotitia/DNA3.0-27B (Korean fine-tune of the same base), with MLP-passthrough to preserve the base model's reasoning-tag policy.

Recipe

Method: DARE-TIES (custom CPU streaming merger), weight 0.5, density 0.53, DAREx-q 0.75, seed 42.
mlp.{gate,up,down}_proj kept verbatim from base (passthrough); attention, linear_attn (Gated-DeltaNet/SSM), norms, embeddings/head merged.
Architecture: Qwen3_5 hybrid (48 linear-attn + 16 full-attn layers), multimodal, 27B, bf16.
Built on a single Apple M4 Pro (64 GB, no GPU) — CPU streaming merge, ~5 min.

Status

This is a v0 test artifact for validating the K-AI leaderboard submission pipeline. It is expected to score near the Qwen/Qwen3.6-27B baseline (the donor differs from base by < 0.3%); it is not a tuned contender.

Downloads last month: 15

Safetensors

Model size

28B params

Tensor type

BF16

Model tree for websfactory/Webs-Ko-27B-v0-test

Qwen/Qwen3.6-27B

dnotitia/DNA3.0-27B

Merge model

this model