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
```

sd24 commited on Jun 1

Commit

f344ae3

verified ·

1 Parent(s): 35055b7

Upload folder using huggingface_hub

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Files changed (3) hide show

README.md +3 -11
model-00001-of-00001.safetensors +1 -1
sharded/shard-00001-model-00001-of-00001.safetensors +1 -1

README.md CHANGED Viewed

@@ -52,22 +52,14 @@ 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).
-messages = [{"role": "user", "content": "<|image|>Describe this 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)
-# use_cache=False: HF's generate() passes a DynamicCache that fla indexes as a
-# list, which the new transformers API no longer supports.
 out = model.generate(**inputs, max_new_tokens=512, temperature=1.0, top_k=10,
                      top_p=1.0, do_sample=True, use_cache=False)
 print(processor.decode(out[0], skip_special_tokens=True))

 repo_id = "<repo_id>"
 model = AutoModelForImageTextToText.from_pretrained(
     repo_id, trust_remote_code=True,
+).cuda()
 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)
+inputs = processor(text="<|image|>An image of", images=image,
+                   return_tensors="pt").to(model.device)
 out = model.generate(**inputs, max_new_tokens=512, temperature=1.0, top_k=10,
                      top_p=1.0, do_sample=True, use_cache=False)
 print(processor.decode(out[0], skip_special_tokens=True))

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