Instructions to use PanocularAI/PanoVLM-500M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PanocularAI/PanoVLM-500M with Transformers:

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

pipe = pipeline("image-text-to-text", model="PanocularAI/PanoVLM-500M", 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 AutoModel
model = AutoModel.from_pretrained("PanocularAI/PanoVLM-500M", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use PanocularAI/PanoVLM-500M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PanocularAI/PanoVLM-500M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PanocularAI/PanoVLM-500M",
		"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/PanocularAI/PanoVLM-500M

SGLang

How to use PanocularAI/PanoVLM-500M 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 "PanocularAI/PanoVLM-500M" \
    --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": "PanocularAI/PanoVLM-500M",
		"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 "PanocularAI/PanoVLM-500M" \
        --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": "PanocularAI/PanoVLM-500M",
		"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 PanocularAI/PanoVLM-500M with Docker Model Runner:
```
docker model run hf.co/PanocularAI/PanoVLM-500M
```

PanoVLM-500M / README.md

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metadata

library_name: transformers
license: apache-2.0
language:
  - en
pipeline_tag: image-text-to-text
tags:
  - panovlm
  - fastvit
  - vision-language
  - linear-attention

PanoVLM-500M

PanoVLM is a linear-attention vision-language model: a FastViT-HD vision encoder (timm) feeding a PanoLM linear-attention causal LM through a lightweight projector.

Type: Vision-Language (image-text-to-text) Model
LM: PanoLM-380M
Vision encoder: FastViT-HD (timm), NCHW input
Projector: BitLinear (encoder dim → LM dim)
Default image resolution: 1024×1024 (pad-resized; only the resolution is meant to be changed)

Parameters

Component	Parameters
PanoLM LM	~387 M
FastViT-HD vision encoder	~123 M
Projector	~3 M
Total	~513 M

Requirements

torch==2.12.0
transformers==5.8.1
flash-linear-attention==0.5.0
timm==1.0.25

Usage

Replace <repo_id> with the HF Hub identifier.

from transformers import AutoModelForImageTextToText, AutoProcessor
from PIL import Image
import requests

repo_id = "<repo_id>"
model = AutoModelForImageTextToText.from_pretrained(
    repo_id, trust_remote_code=True,
).cuda()  # fla's RMSNorm uses Triton kernels that only run on CUDA tensors.
processor = AutoProcessor.from_pretrained(repo_id, trust_remote_code=True)

url = "https://llava-vl.github.io/static/images/view.jpg"
image = Image.open(requests.get(url, stream=True).raw)

# PanoVLM's chat template wraps string content, so put the <|image|> placeholder
# inline in the message text (the processor expands it into the image tokens).
# Keep the space after <|image|>: the HF tokenizer, unlike the training tokenizer,
# does not implicitly insert one at the special-token boundary.
messages = [{"role": "user", "content": "<|image|> Is there a boat in the image?"}]
prompt = processor.tokenizer.apply_chat_template(
    messages, add_generation_prompt=True, tokenize=False,
)
inputs = processor(text=prompt, images=image, return_tensors="pt").to(model.device)

out = model.generate(**inputs, max_new_tokens=512)
print(processor.decode(out[0], skip_special_tokens=True))