Instructions to use ayjays132/Phillnet-Mini-Max with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ayjays132/Phillnet-Mini-Max with Transformers:

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

pipe = pipeline("image-text-to-text", model="ayjays132/Phillnet-Mini-Max", 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("ayjays132/Phillnet-Mini-Max", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ayjays132/Phillnet-Mini-Max with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ayjays132/Phillnet-Mini-Max"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ayjays132/Phillnet-Mini-Max",
		"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/ayjays132/Phillnet-Mini-Max

SGLang

How to use ayjays132/Phillnet-Mini-Max 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 "ayjays132/Phillnet-Mini-Max" \
    --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": "ayjays132/Phillnet-Mini-Max",
		"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 "ayjays132/Phillnet-Mini-Max" \
        --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": "ayjays132/Phillnet-Mini-Max",
		"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 ayjays132/Phillnet-Mini-Max with Docker Model Runner:
```
docker model run hf.co/ayjays132/Phillnet-Mini-Max
```

Phillnet-Mini-Max / RELEASE_MANIFEST.json

ayjays132

Release Phillnet Mini Text-Vision v1.1.0

c33608b verified 1 day ago

Raw

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1.61 kB

	{
	"schema_version": "1.0",
	"release": {
	"name": "Phillnet Mini Text-Vision",
	"version": "1.1.0",
	"release_date": "2026-08-21",
	"license": "Apache-2.0",
	"source_model": "https://huggingface.co/ayjays132/Phillnet-Mini-Omni-Max"
	},
	"repository": {
	"upload_root": true,
	"file_count_excluding_this_manifest": 48,
	"size_bytes_excluding_this_manifest": 1785015195,
	"lfs_artifacts": [
	"model.safetensors",
	"tokenizer.json"
	]
	},
	"artifacts": {
	"model.safetensors": {
	"bytes": 1763655304,
	"sha256": "f1a913f99f8ce921c1aa79982a09eaee6744448766f09a4756751e2d4b9342fc"
	},
	"tokenizer.json": {
	"bytes": 19989325,
	"sha256": "06b9509352d2af50381ab2247e083b80d32d5c0aba91c272ca9ff729b6a0e523"
	}
	},
	"capabilities": {
	"enabled": [
	"text-generation",
	"still-image-understanding"
	],
	"disabled": [
	"sdxl-image-generation",
	"video-generation",
	"audio",
	"tools",
	"agents",
	"remote-image-url-fetching"
	]
	},
	"inference_defaults": {
	"reasoning_effort": "max",
	"adaptive_private_reasoning": true,
	"private_reasoning_policy": "adaptive_context",
	"max_visible_answer_tokens": 8192,
	"active_context_window_tokens": 32768
	},
	"deployment": {
	"container": "Dockerfile",
	"compose": "docker-compose.yml",
	"service": "server.py",
	"authentication": "PHILLNET_API_KEY (optional locally, required for public deployment)",
	"api_route": "/v1/chat/completions",
	"health_route": "/health",
	"readiness_route": "/ready"
	}
	}