Instructions to use 169Pi/Alpie_seed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use 169Pi/Alpie_seed with Transformers:

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

pipe = pipeline("image-text-to-text", model="169Pi/Alpie_seed")
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 AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("169Pi/Alpie_seed")
model = AutoModelForMultimodalLM.from_pretrained("169Pi/Alpie_seed", 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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use 169Pi/Alpie_seed with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "169Pi/Alpie_seed"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "169Pi/Alpie_seed",
		"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/169Pi/Alpie_seed

SGLang

How to use 169Pi/Alpie_seed 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 "169Pi/Alpie_seed" \
    --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": "169Pi/Alpie_seed",
		"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 "169Pi/Alpie_seed" \
        --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": "169Pi/Alpie_seed",
		"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 169Pi/Alpie_seed with Docker Model Runner:
```
docker model run hf.co/169Pi/Alpie_seed
```

deepanshupillm commited on about 7 hours ago

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Update README.md

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@@ -52,6 +52,12 @@ tags:
   <a href="https://www.instagram.com/169pi.ai/"><img src="https://img.shields.io/badge/📸_Instagram-@169pi.ai-D946EF?style=flat&labelColor=4A044E" alt="Instagram"></a>
 </p>
 </div>
 ---
@@ -108,14 +114,6 @@ ollama run 169pi/alpie-seed:vision
 Alpie Seed 9B is available on Kaggle Models: https://www.kaggle.com/models/alpie/alpie-seed-9b
-## Demo Video
-A short demo of Alpie Seed running fully offline on a mobile device, tested on [PocketPal](https://github.com/a-ghorbani/pocketpal-ai). It shows the quantized model generating responses on-device with no cloud connection.
-<video controls playsinline preload="metadata" width="354" height="199" style="width:354px; height:199px; max-width:100%; object-fit:contain; background:#000;" src="https://huggingface.co/169Pi/Alpie_seed/resolve/main/alpie_offline_mobile_demo_fast.mp4"></video>
-[▶️ Watch the demo video](https://huggingface.co/169Pi/Alpie_seed/resolve/main/alpie_offline_mobile_demo_fast.mp4)
 ## Model Overview
 - Type: Causal Language Model with Vision Encoder

   <a href="https://www.instagram.com/169pi.ai/"><img src="https://img.shields.io/badge/📸_Instagram-@169pi.ai-D946EF?style=flat&labelColor=4A044E" alt="Instagram"></a>
 </p>
+A short demo of Alpie Seed running fully offline on a mobile device, tested on [PocketPal](https://github.com/a-ghorbani/pocketpal-ai). It shows the quantized model generating responses on-device with no cloud connection.
+<video controls playsinline preload="metadata" width="354" height="199" style="width:354px; height:199px; max-width:100%; object-fit:contain; background:#000;" src="https://huggingface.co/169Pi/Alpie_seed/resolve/main/alpie_offline_mobile_demo_fast.mp4"></video>
+[▶️ Watch the demo video](https://huggingface.co/169Pi/Alpie_seed/resolve/main/alpie_offline_mobile_demo_fast.mp4)
 </div>
 ---
 Alpie Seed 9B is available on Kaggle Models: https://www.kaggle.com/models/alpie/alpie-seed-9b
 ## Model Overview
 - Type: Causal Language Model with Vision Encoder