Instructions to use InterleaveThinker/InterleaveThinker-Planner-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use InterleaveThinker/InterleaveThinker-Planner-8B with Transformers:

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

pipe = pipeline("image-text-to-text", model="InterleaveThinker/InterleaveThinker-Planner-8B")
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 AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("InterleaveThinker/InterleaveThinker-Planner-8B")
model = AutoModelForImageTextToText.from_pretrained("InterleaveThinker/InterleaveThinker-Planner-8B")
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?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use InterleaveThinker/InterleaveThinker-Planner-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "InterleaveThinker/InterleaveThinker-Planner-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "InterleaveThinker/InterleaveThinker-Planner-8B",
		"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/InterleaveThinker/InterleaveThinker-Planner-8B

SGLang

How to use InterleaveThinker/InterleaveThinker-Planner-8B 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 "InterleaveThinker/InterleaveThinker-Planner-8B" \
    --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": "InterleaveThinker/InterleaveThinker-Planner-8B",
		"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 "InterleaveThinker/InterleaveThinker-Planner-8B" \
        --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": "InterleaveThinker/InterleaveThinker-Planner-8B",
		"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 InterleaveThinker/InterleaveThinker-Planner-8B with Docker Model Runner:
```
docker model run hf.co/InterleaveThinker/InterleaveThinker-Planner-8B
```

InterleaveThinker-Planner-8B / README.md

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	---
	base_model:
	- Qwen/Qwen3-VL-8B-Instruct
	datasets:
	- InterleaveThinker/Train-Data
	library_name: transformers
	pipeline_tag: image-text-to-text
	license: apache-2.0
	---

	# InterleaveThinker-Planner Model

	This repository contains the InterleaveThinker-Planner-8B model presented in [InterleaveThinker: Reinforcing Agentic Interleaved Generation]().

	[Project Page]() \| [GitHub Repository](https://github.com/zhengdian1/InterleaveThinker) \| [Paper]()

	# 👀 Intro

	<div align="center">
	<img src="https://github.com/zhengdian1/InterleaveThinker/blob/main/assets/teaser.jpg?raw=true" alt="InterleaveThinker Teaser" width="80%">
	</div>

	We introduce InterleaveThinker, as the first multi-agent pipeline designed to endow any existing image generator with interleaved generation capabilities. InterleaveThinker can organize the image-text input sequence via a planner agent, evaluate generator outputs, identify deviations, and refine instructions via a critic agent, enabling complex interleaved text-image sequence generation for visual narratives, guidance, embodied manipulation and long-horizon sub-task annotation.

	We build three dedicated training datasets—Interleave-Planner-SFT-80k, Interleave-Critic-SFT-112k, and Interleave-Critic-RL-13k—for interleaved generation and step-wise instruction correction using GRPO with proposed accuracy and step-wise rewards.

	InterleaveThinker achieves performance comparable to Nano Banana and GPT-5 on interleaved generation benchmarks, delivering substantial gains on reasoning-based benchmarks (e.g., boosting WISE from 0.47 to 0.74 and RISE from 13.3 to 28.9 on 4-step FLUX.2-klein). It also demonstrates strong transferability, improving performance across various existing image generators.


	## 🎥 Demo

	#### Inference Process Example

	<div align="center">
	<img src="https://github.com/zhengdian1/InterleaveThinker/blob/main/assets/example.jpg?raw=true" alt="Inference Process Example" width="85%">
	</div>

	For more examples, please refer to our website [[🌐Project Page]]()

	## 🚀 Training and Inference

	For detailed instructions on setup, SFT/RL training, and inference, please refer to the [official GitHub repository](https://github.com/zhengdian1/InterleaveThinker).

	## 📐 Citation

	If you find our work helpful for your research, please consider citing our work:

	```bibtex
	@article{zheng2026interleavethinker,
	title={InterleaveThinker: Reinforcing Agentic Interleaved Generation},
	author={Zheng, Dian and Li, Hongyu and Zhang, Manyuan and Feng, Kaituo and Li, Hongsheng},
	journal={},
	year={2026}
	}
	```