Instructions to use SwarmDo/SwarmDo-A2-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SwarmDo/SwarmDo-A2-merged with Transformers:

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

pipe = pipeline("image-text-to-text", model="SwarmDo/SwarmDo-A2-merged")
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("SwarmDo/SwarmDo-A2-merged")
model = AutoModelForMultimodalLM.from_pretrained("SwarmDo/SwarmDo-A2-merged", 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 SwarmDo/SwarmDo-A2-merged with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SwarmDo/SwarmDo-A2-merged"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SwarmDo/SwarmDo-A2-merged",
		"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/SwarmDo/SwarmDo-A2-merged

SGLang

How to use SwarmDo/SwarmDo-A2-merged 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 "SwarmDo/SwarmDo-A2-merged" \
    --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": "SwarmDo/SwarmDo-A2-merged",
		"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 "SwarmDo/SwarmDo-A2-merged" \
        --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": "SwarmDo/SwarmDo-A2-merged",
		"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 SwarmDo/SwarmDo-A2-merged with Docker Model Runner:
```
docker model run hf.co/SwarmDo/SwarmDo-A2-merged
```

SwarmDo-A2 — merged weights (turnkey, code + vision in one model)

Pre-merged, ready-to-serve weights of SwarmDo-A2. The combined coding + render-verified visual-coding adapter is already merged into Qwen/Qwen3.6-27B with the vision tower intact, so you can serve it directly with vLLM — no merge step needed.

vllm serve SwarmDo/SwarmDo-A2-merged --served-model-name swarmdo-a2 \
  --enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3 \
  --enforce-eager --trust-remote-code

Then call it as an OpenAI-compatible chat endpoint (model = "swarmdo-a2"); pass an image via the standard image_url block for visual-coding (image → code).

One model, both skills — fixes real bugs (execution-verified) and writes code from images (render-verified), with measured proof that combining the two degrades neither (the "churn gate").

Full model card, results & methodology: SwarmDo/SwarmDo-A2
The adapter (LoRA form, for re-merging with your own settings): same repo
Public hub: github.com/SwarmDo/models (a2/)

Notes: serve as these merged weights (native fast path). Do not load A2 as a vLLM LoRA — the adapter reaches the base's gated-DeltaNet modules, which JIT-hang under vLLM's LoRA path. There is no text GGUF for A2 (quantizing to GGUF drops the vision tower). Apache-2.0.

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Safetensors

Model size

27B params

Tensor type

BF16

Model tree for SwarmDo/SwarmDo-A2-merged

Qwen/Qwen3.6-27B

Merge model

this model