Instructions to use UCSC-VLAA/VLM-CapCurriculum-InternVL3-8B-Staged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use UCSC-VLAA/VLM-CapCurriculum-InternVL3-8B-Staged with Transformers:

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

pipe = pipeline("image-text-to-text", model="UCSC-VLAA/VLM-CapCurriculum-InternVL3-8B-Staged")
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("UCSC-VLAA/VLM-CapCurriculum-InternVL3-8B-Staged")
model = AutoModelForImageTextToText.from_pretrained("UCSC-VLAA/VLM-CapCurriculum-InternVL3-8B-Staged")
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 UCSC-VLAA/VLM-CapCurriculum-InternVL3-8B-Staged with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "UCSC-VLAA/VLM-CapCurriculum-InternVL3-8B-Staged"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "UCSC-VLAA/VLM-CapCurriculum-InternVL3-8B-Staged",
		"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/UCSC-VLAA/VLM-CapCurriculum-InternVL3-8B-Staged

SGLang

How to use UCSC-VLAA/VLM-CapCurriculum-InternVL3-8B-Staged 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 "UCSC-VLAA/VLM-CapCurriculum-InternVL3-8B-Staged" \
    --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": "UCSC-VLAA/VLM-CapCurriculum-InternVL3-8B-Staged",
		"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 "UCSC-VLAA/VLM-CapCurriculum-InternVL3-8B-Staged" \
        --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": "UCSC-VLAA/VLM-CapCurriculum-InternVL3-8B-Staged",
		"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 UCSC-VLAA/VLM-CapCurriculum-InternVL3-8B-Staged with Docker Model Runner:
```
docker model run hf.co/UCSC-VLAA/VLM-CapCurriculum-InternVL3-8B-Staged
```

VLM-CapCurriculum-InternVL3-8B-Staged

A vision-language model post-trained from OpenGVLab/InternVL3-8B-hf with the staged, capability-dimension curriculum from "From Seeing to Thinking: Decoupling Perception and Reasoning Improves Post-Training of Vision-Language Models" (ICML 2026).

This release is the Stage-3 step-186 checkpoint, which gave the largest gain over the merged baseline among the four backbones we tried.

TL;DR. Visual perception — not reasoning length — is the dominant bottleneck for visual reasoning in VLMs. We fix this by post-training along a capability axis (perception → textual reasoning → visual reasoning) rather than mixing all data together.

Resource	Link
📄 Paper
💻 Code	https://github.com/UCSC-VLAA/VLM-CapCurriculum
🌐 Project page	https://ucsc-vlaa.github.io/VLM-CapCurriculum
🤗 Collection (model + data + eval)	https://huggingface.co/collections/UCSC-VLAA/vlm-capcurriculum-from-seeing-to-thinking-icml-2026-6a07691f944148ccb2b183b8

Headline numbers (extended benchmark suite, AVG over 10 benchmarks)

Setting	Extended AVG
InternVL3-8B (base)	29.69
InternVL3-8B + Merged training	41.94
InternVL3-8B + Staged (this model)	45.71

Δ over merged: +3.77 — the largest staged-vs-merged gap among the four backbones. Most striking is WeMath (+9.90 over merged), evidence that decoupling perception and reasoning is especially impactful for weaker base models. See Appendix Table 9 of the paper for the full per-benchmark breakdown.

How it was trained

Three RLVR stages with GRPO (on top of EasyR1):

Stage 1 — visual perception on UCSC-VLAA/VLM-CapCurriculum-Perception (synthesised + filtered DOCCI MCQs).
Stage 2 — textual reasoning on UCSC-VLAA/VLM-CapCurriculum-TextReasoning (ORZ-Math-13k).
Stage 3 — visual reasoning on UCSC-VLAA/VLM-CapCurriculum-VisualReasoning (CLEVR-Math + GeoQA170K + Math PUMA + DocVQA + ArxivQA mix). Released checkpoint: step 186 of Stage 3.

InternVL3 needs damped optimisation in Stage 2 to avoid entropy explosion (lr=3e-7, kl=5e-2, clip=0.15, max_grad_norm=0.5) — these are baked into the launch scripts.

All three stages share one system / format prompt — see Inference below.

Detailed launch scripts: training/examples/internvl3_8b/ in the code repo.

Inference

The model expects the unified system prompt that it was trained against:

You FIRST think about the reasoning process as an internal monologue and then
provide the final answer. The reasoning process MUST BE enclosed within
<think> </think> tags. The final answer MUST BE put in \boxed{}.
i.e. <think> reasoning here </think> \boxed{final answer here}

Quick start with LMDeploy (the InternVL family is served via LMDeploy in our setup):

lmdeploy serve api_server UCSC-VLAA/VLM-CapCurriculum-InternVL3-8B-Staged \
  --server-port 23342 --tp 4

For VLMEvalKit-style benchmark eval, plug it in via the InternVL3_8B_Staged alias defined in evaluation/configs/models.py.

Intended use & limitations

Intended for research on vision-language reasoning, post-training methodology, and capability-dimension curriculum learning. Inherits the safety / bias profile of the underlying InternVL3-8B backbone; we have not added additional alignment fine-tuning. Not recommended for high-stakes deployments without further evaluation.

Trained at the 8B parameter scale with 4096-token max prompt length and a fixed group size of 5. Behaviour at much longer contexts or substantially different prompt formats has not been characterised.

License & citation

Released under Apache-2.0, matching the upstream backbone. If you use this model, please cite:

@inproceedings{vlmcapcurriculum2026,
  title  = {From Seeing to Thinking: Decoupling Perception and Reasoning Improves Post-Training of Vision-Language Models},
  author = {Juncheng Wu and Hardy Chen and Haoqin Tu and Xianfeng Tang and Freda Shi and Hui Liu and Hanqing Lu and Cihang Xie and Yuyin Zhou},
  booktitle = {Proceedings of the International Conference on Machine Learning (ICML)},
  year   = {2026}
}