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_VALIDATION.md

ayjays132

Release Phillnet Mini Text-Vision v1.1.0

c33608b verified 1 day ago

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Release Validation

Release: Phillnet Mini Text-Vision v1.1.0
Date: 21 August 2026

Completed checks

Check	Result
Custom model Python modules	All root Python modules compile successfully.
`config.json` and `RELEASE_MANIFEST.json`	Valid JSON.
Retained model checkpoint	SHA-256 matches the value in `RELEASE_MANIFEST.json`.
Example gallery	All three HTML and PNG assets are present.
Hugging Face large-file policy	`model.safetensors` and `tokenizer.json` are explicitly marked for LFS.
Repository hygiene	`.gitignore` excludes bytecode caches, secrets, logs, and archives.
Production files	`Dockerfile`, `docker-compose.yml`, `.env.example`, `PRODUCTION.md`, and `HF_UPLOAD.md` are present.
API-key gate	Missing credentials are rejected when `PHILLNET_API_KEY` is set; bearer and `X-API-Key` forms are accepted.
Request safeguards	Maximum image byte, pixel, and request-body limits were validated.
Temporary deployment	Fresh `/health` and `/ready` checks returned a ready text-and-vision-only service.

Validation boundary

The available environment does not include Docker, so the container image and Compose runtime were inspected and validated statically rather than built locally. The Python service was compiled and loaded directly with the full model checkpoint, then checked through its HTTP health and readiness routes.

Upload status

The repository is ready to upload as a complete Hugging Face model repository. Follow HF_UPLOAD.md from the parent directory and verify the checksum in RELEASE_MANIFEST.json after transfer.