Instructions to use XLearning-SCU/HERO-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use XLearning-SCU/HERO-9B with Transformers:

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

pipe = pipeline("image-text-to-text", model="XLearning-SCU/HERO-9B")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("XLearning-SCU/HERO-9B")
model = AutoModelForMultimodalLM.from_pretrained("XLearning-SCU/HERO-9B", device_map="auto")
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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use XLearning-SCU/HERO-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "XLearning-SCU/HERO-9B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "XLearning-SCU/HERO-9B",
		"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/XLearning-SCU/HERO-9B

SGLang

How to use XLearning-SCU/HERO-9B 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 "XLearning-SCU/HERO-9B" \
    --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": "XLearning-SCU/HERO-9B",
		"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 "XLearning-SCU/HERO-9B" \
        --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": "XLearning-SCU/HERO-9B",
		"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 XLearning-SCU/HERO-9B with Docker Model Runner:
```
docker model run hf.co/XLearning-SCU/HERO-9B
```

HERO-9B / README.md

XLearning-SCU

Upload HERO-9B release

1ba7885 verified 1 day ago

preview code

Raw History Blame Contribute Delete

3.52 kB

	---
	library_name: transformers
	license: apache-2.0
	license_link: https://huggingface.co/Qwen/Qwen3.5-9B/blob/main/LICENSE
	pipeline_tag: image-text-to-text
	base_model: Qwen/Qwen3.5-9B
	base_model_relation: finetune
	tags:
	- HERO
	- reinforcement-learning
	- coding-agent
	- token-efficiency
	---

	<p align="center">
	<img src="assets/hero-logo.png" alt="HERO logo" width="760">
	</p>

	<h1 align="center">HERO-9B</h1>

	<p align="center">
	<strong>Doing More with Less Tokens: Hierarchical Reinforcement Learning for Efficient Coding Agents</strong>
	</p>

	<p align="center">
	<a href="https://arxiv.org/abs/2609.38885"><img src="https://img.shields.io/badge/arXiv-2609.38885-b31b1b.svg" alt="Paper"></a>
	<a href="https://github.com/XLearning-SCU/HERO"><img src="https://img.shields.io/badge/GitHub-Code-181717.svg" alt="Code"></a>
	<a href="https://huggingface.co/collections/XLearning-SCU/hero"><img src="https://img.shields.io/badge/%F0%9F%A4%97-Models-f4c430.svg" alt="Hugging Face Models"></a>
	<a href="https://huggingface.co/datasets/XLearning-SCU/HERO-Trajectories"><img src="https://img.shields.io/badge/%F0%9F%A4%97-Trajectories-f4c430.svg" alt="Hugging Face Trajectories"></a>
	</p>

	## 🔍 Overview

	HERO-9B is post-trained from Qwen3.5-9B using HERO (HiErarchical ReinfOrcement learning) to improve token efficiency while prioritizing task resolution.

	\| Item \| Description \|
	\| --- \| --- \|
	\| Base model \| `Qwen/Qwen3.5-9B` \|
	\| Parameters \| 9B (dense) \|
	\| Architecture and tokenizer \| Inherited from Qwen3.5-9B \|
	\| Training \| HERO on 640 multilingual SWE tasks from SWE-Gym, Multi-SWE-bench, and SWE-rebench \|
	\| Intended use \| Repository-level coding agents \|
	\| Format \| Hugging Face weights and tokenizer \|

	HERO combines capability-based efficiency gating, resolution-first clipping,
	and efficiency credit at trajectory and turn levels. Qwen3.5 is the backbone;
	this release contains the HERO post-trained weights.

	## Usage

	Follow the [preparation guide](https://github.com/XLearning-SCU/HERO/blob/main/Prepare.md) in the HERO code repository. From that repository's root,
	with this model saved under `../models/HERO-9B/`:

	```bash
	MODEL=../models/HERO-9B bash eval/run_eval_swebench_verified.sh
	MODEL=../models/HERO-9B bash eval/run_eval_swebench_multilingual.sh
	```

	## Evaluation Settings

	The released evaluation scripts use the following defaults for both SWE-bench Verified and SWE-bench Multilingual:

	\| Setting \| Value \|
	\| --- \| --- \|
	\| Agent scaffold \| Claude Code \|
	\| Inference backend \| SGLang \|
	\| Temperature \| 0.6 \|
	\| Top-p \| 0.95 \|
	\| Context length \| 131,072 tokens \|
	\| Maximum output per call \| 16,000 tokens \|
	\| Maximum agent turns \| 200 \|
	\| Automatic context compaction \| Disabled \|
	\| Tools \| Default tools, excluding WebFetch, WebSearch, and Agent \|

	See the [evaluation script](https://github.com/XLearning-SCU/HERO/blob/main/eval/run_eval_swe_bench.sh) for configuration options.

	## License

	Apache-2.0. See `LICENSE`. We acknowledge the Qwen team for the base model.

	## 📖 Citation

	If you find HERO useful, please cite our [paper](https://arxiv.org/abs/2609.38885):

	```bibtex
	@misc{li2026hero,
	title = {Doing More with Less Tokens: Hierarchical Reinforcement Learning for Efficient Coding Agents},
	author = {Haobin Li and Liang Jiang and Zhenyu Huang and Mouxing Yang and Xi Peng},
	year = {2026},
	eprint = {2609.38885},
	archivePrefix = {arXiv},
	primaryClass = {cs.SE},
	url = {https://arxiv.org/abs/2609.38885}
	}
	```