Instructions to use modilify/Modilify-Mk1-preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use modilify/Modilify-Mk1-preview with Transformers:

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

pipe = pipeline("image-text-to-text", model="modilify/Modilify-Mk1-preview", trust_remote_code=True)
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 AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("modilify/Modilify-Mk1-preview", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use modilify/Modilify-Mk1-preview with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "modilify/Modilify-Mk1-preview"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "modilify/Modilify-Mk1-preview",
		"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/modilify/Modilify-Mk1-preview

SGLang

How to use modilify/Modilify-Mk1-preview 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 "modilify/Modilify-Mk1-preview" \
    --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": "modilify/Modilify-Mk1-preview",
		"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 "modilify/Modilify-Mk1-preview" \
        --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": "modilify/Modilify-Mk1-preview",
		"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 modilify/Modilify-Mk1-preview with Docker Model Runner:
```
docker model run hf.co/modilify/Modilify-Mk1-preview
```

ydy9038074 commited on 5 days ago

Commit

dd028b0

verified ·

1 Parent(s): 6c70261

Publish Modilify Mk1 Preview

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README.md +2 -6

README.md CHANGED Viewed

@@ -124,8 +124,7 @@ output = model.generate(**inputs, max_new_tokens=256)
 ### Video-frame input
-The processor represents video as a sampled sequence of frames. The following
-example uses PyAV to decode a short local clip and samples at most 32 RGB frames.
 ```python
 import av
@@ -198,10 +197,7 @@ model = AutoModelForMultimodalLM.from_pretrained(
 )
 ```
-Generation supports left-padded batches with independent stopping and
-`generated_lengths` for every row. Batch prompts of similar lengths together
-for the best throughput; KV-cache and canvas memory grow with batch size.
-Streaming and caller-supplied KV caches remain limited to batch size 1.
 ## Details

 ### Video-frame input
+The processor represents video as a sampled sequence of frames. The following example uses PyAV to decode a short local clip and samples at most 32 RGB frames.
 ```python
 import av
 )
 ```
+Generation supports left-padded batches with independent stopping and `generated_lengths` for every row. Batch prompts of similar lengths together for the best throughput; KV-cache and canvas memory grow with batch size. Streaming and caller-supplied KV caches remain limited to batch size 1.
 ## Details