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 / STRIP_MANIFEST.json

ayjays132

Complete Phillnet Mini Text-Vision release v1.1.0

1e114b1 verified 1 day ago

Raw

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1.71 kB

	{
	"source": "/home/ubuntu/phillnet_work/Phillnet-Mini-Omni-Max.original",
	"target": "/home/ubuntu/phillnet_work/Phillnet-Mini-Text-Vision",
	"removed_top_level_entries": [
	"examples",
	"generation-acceleration-validation.json",
	"generation-acceleration.json",
	"generation_acceleration.py",
	"orchestration.py",
	"phillnet3_bridge.py",
	"phillnet3_exact_sdxl_manifest.json",
	"phillnet3_exact_sdxl_release.json",
	"phillnet3_sdxl",
	"phillnet3_sdxl.py",
	"reasoning_runtime.py",
	"requirements-image.txt",
	"requirements-optional-tools.txt",
	"smolagents_runtime.py",
	"synthesis.py",
	"tooling.py"
	],
	"removed_sdxl_config_keys": [
	"generation_acceleration_backend",
	"generation_architecture",
	"generation_beta_end",
	"generation_beta_start",
	"generation_clip_chunk_budget",
	"generation_compile_mode",
	"generation_conv_low_bit",
	"generation_cross_attention_heads",
	"generation_decode_chunk_size",
	"generation_diffusion_steps",
	"generation_latent_channels",
	"generation_latent_scaling_factor",
	"generation_max_frames",
	"generation_perceptual_size",
	"generation_phillnet3_native_manifest",
	"generation_prediction_type",
	"generation_quality_strength",
	"generation_res_blocks_per_stage",
	"generation_scheduler_type",
	"generation_sdxl_pooled_dim",
	"generation_sdxl_time_embed_dim",
	"generation_sdxl_token_dim",
	"generation_timestep_spacing",
	"generation_train_timesteps",
	"generation_unet_channels",
	"generation_vae_channels",
	"generation_vae_scale_factor"
	],
	"retained_capabilities": [
	"text-generation",
	"image-understanding"
	]
	}