Instructions to use miraclezqc/TestForDots3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use miraclezqc/TestForDots3 with Transformers:

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

pipe = pipeline("image-text-to-text", model="miraclezqc/TestForDots3")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForSeq2SeqLM
model = AutoModelForSeq2SeqLM.from_pretrained("miraclezqc/TestForDots3", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use miraclezqc/TestForDots3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "miraclezqc/TestForDots3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "miraclezqc/TestForDots3",
		"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/miraclezqc/TestForDots3

SGLang

How to use miraclezqc/TestForDots3 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 "miraclezqc/TestForDots3" \
    --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": "miraclezqc/TestForDots3",
		"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 "miraclezqc/TestForDots3" \
        --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": "miraclezqc/TestForDots3",
		"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 miraclezqc/TestForDots3 with Docker Model Runner:
```
docker model run hf.co/miraclezqc/TestForDots3
```

TestForDots3

File size: 1,645 Bytes

14258a1

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  "feature_extractor": {
    "chunk_length": 1,
    "dither": 0.0,
    "feature_extractor_type": "Dots3NoteFeatureExtractor",
    "feature_size": 8,
    "hop_length": 160,
    "n_fft": 400,
    "n_samples": 16000,
    "nb_max_frames": 100,
    "padding_side": "right",
    "padding_value": 0.0,
    "return_attention_mask": false,
    "sampling_rate": 16000
  },
  "image_processor": {
    "do_convert_rgb": true,
    "do_normalize": true,
    "do_rescale": true,
    "do_resize": true,
    "image_mean": [
      0.48145466,
      0.4578275,
      0.40821073
    ],
    "image_processor_type": "Qwen2VLImageProcessor",
    "image_std": [
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    ],
    "merge_size": 2,
    "patch_size": 2,
    "resample": 3,
    "rescale_factor": 0.00392156862745098,
    "size": {
      "longest_edge": 64,
      "shortest_edge": 16
    },
    "temporal_patch_size": 1
  },
  "processor_class": "Dots3NoteProcessor",
  "video_processor": {
    "do_convert_rgb": true,
    "do_normalize": true,
    "do_rescale": true,
    "do_resize": true,
    "do_sample_frames": true,
    "fps": 1,
    "image_mean": [
      0.48145466,
      0.4578275,
      0.40821073
    ],
    "image_std": [
      0.26862954,
      0.26130258,
      0.27577711
    ],
    "max_frames": 8,
    "max_video_tokens": 32,
    "merge_size": 2,
    "min_frames": 4,
    "patch_size": 2,
    "resample": 3,
    "rescale_factor": 0.00392156862745098,
    "return_metadata": false,
    "size": {
      "longest_edge": 64,
      "shortest_edge": 16
    },
    "temporal_patch_size": 1,
    "video_processor_type": "Qwen3VLVideoProcessor"
  }
}