Instructions to use PerceptronAI/Isaac-0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PerceptronAI/Isaac-0.1 with Transformers:

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

pipe = pipeline("image-text-to-text", model="PerceptronAI/Isaac-0.1", 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("PerceptronAI/Isaac-0.1", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use PerceptronAI/Isaac-0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PerceptronAI/Isaac-0.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PerceptronAI/Isaac-0.1",
		"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/PerceptronAI/Isaac-0.1

SGLang

How to use PerceptronAI/Isaac-0.1 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 "PerceptronAI/Isaac-0.1" \
    --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": "PerceptronAI/Isaac-0.1",
		"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 "PerceptronAI/Isaac-0.1" \
        --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": "PerceptronAI/Isaac-0.1",
		"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 PerceptronAI/Isaac-0.1 with Docker Model Runner:
```
docker model run hf.co/PerceptronAI/Isaac-0.1
```

Upload modular_isaac.py

by merve HF Staff - opened Sep 19, 2025

base: refs/heads/main

←

from: refs/pr/4

Discussion Files changed

+943

-21

merve

Sep 19, 2025

•

edited Sep 19, 2025

Hello and congrats for the release!
This PR makes this model load with no additional dependency, only transformers, which is very convenient for the users:

from transformers import AutoTokenizer, AutoConfig, AutoModelForCausalLM, AutoProcessor

tokenizer = AutoTokenizer.from_pretrained("Perceptron/Isaac-0.1", trust_remote_code=True, use_fast=False)
config = AutoConfig.from_pretrained("Perceptron/Isaac-0.1", trust_remote_code=True)
processor = AutoProcessor.from_pretrained("Perceptron/Isaac-0.1", tokenizer=tokenizer, config=config)
model = AutoModelForCausalLM.from_pretrained("Perceptron/Isaac-0.1", trust_remote_code=True)

you can also add a small inference notebook I made by replacing the username to Perceptron: https://colab.research.google.com/drive/1BHl2ZT8cYZ0HlP_q4HllFuCXWIBX_R_2?usp=sharing

if you add the "notebook.ipynb" repo to it's one-click open in the repository, making it easier for people to try out your model as well!

Upload modular_isaac.py6862a3fc

akshat-perceptron

Perceptron AI org Sep 19, 2025

What is your recommendation around this for the core transformers repo: https://github.com/huggingface/transformers/pull/40962

TensorStream is a core abstraction for us which we will continue to optimize and improve - our intention of keeping it in the perceptron package was to make it easier to centralize improvements across open code bases.

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modular_isaac.py

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