Instructions to use DavidSeyserHF/Iris1.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DavidSeyserHF/Iris1.5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="DavidSeyserHF/Iris1.5")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("DavidSeyserHF/Iris1.5", dtype="auto") - PEFT
How to use DavidSeyserHF/Iris1.5 with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use DavidSeyserHF/Iris1.5 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "DavidSeyserHF/Iris1.5" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "DavidSeyserHF/Iris1.5", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/DavidSeyserHF/Iris1.5
- SGLang
How to use DavidSeyserHF/Iris1.5 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 "DavidSeyserHF/Iris1.5" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "DavidSeyserHF/Iris1.5", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "DavidSeyserHF/Iris1.5" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "DavidSeyserHF/Iris1.5", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use DavidSeyserHF/Iris1.5 with Docker Model Runner:
docker model run hf.co/DavidSeyserHF/Iris1.5
Iris1.5
Iris1.5 is a looped vision-language model built on top of:
ByteDance/Ouro-1.4Bfor language generationNVlabs/RADIOradio_v2.5-hfor image encoding
This repository does not include the full base models. It contains the trained IRIS components only: the vision-to-language projector, dual LoRA adapters, and bundle metadata needed to load them.
Examples
| Tree | Food |
|---|---|
|
|
PromptWhat do you see? Answer briefly.
|
PromptWhat food is shown in the image? Answer briefly.
|
Outputtree
|
Outputramen
|
Quick Start
Install the requirements from this repository, then run inference with the bundled adapter weights:
python inference/infer_hf_bundle.py \
--repo-id DavidSeyserHF/Iris1.5 \
--bundle-subdir hf_bundle_step115000 \
--image tree.jpg \
--prompt "What do you see? Answer briefly."
On the first run, the base Ouro and RADIO models are downloaded and cached automatically.
License
This repository provides adapter and projector deltas only. Commercial use is restricted by the RADIO dependency license, NVIDIA Source Code License-NC: https://github.com/NVlabs/RADIO/blob/main/LICENSE
Usage is also subject to the licenses and terms of:
ByteDance/Ouro-1.4BNVlabs/RADIO- any datasets used during training
Citation
If you use this release, please cite the model repository and commit hash for reproducibility.
@misc{seyser2026iris1-5,
title = {Iris1.5},
author = {David Seyser},
year = {2026},
howpublished = {Hugging Face model repository},
url = {https://huggingface.co/DavidSeyserHF/Iris1.5}
}
Model tree for DavidSeyserHF/Iris1.5
Base model
ByteDance/Ouro-1.4B