Instructions to use XYZAILab/XYZ-Aquila-pro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use XYZAILab/XYZ-Aquila-pro with Transformers:

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

pipe = pipeline("text-generation", model="XYZAILab/XYZ-Aquila-pro")
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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("XYZAILab/XYZ-Aquila-pro")
model = AutoModelForMultimodalLM.from_pretrained("XYZAILab/XYZ-Aquila-pro", 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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use XYZAILab/XYZ-Aquila-pro with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "XYZAILab/XYZ-Aquila-pro"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "XYZAILab/XYZ-Aquila-pro",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/XYZAILab/XYZ-Aquila-pro

SGLang

How to use XYZAILab/XYZ-Aquila-pro 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 "XYZAILab/XYZ-Aquila-pro" \
    --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": "XYZAILab/XYZ-Aquila-pro",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "XYZAILab/XYZ-Aquila-pro" \
        --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": "XYZAILab/XYZ-Aquila-pro",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use XYZAILab/XYZ-Aquila-pro with Docker Model Runner:
```
docker model run hf.co/XYZAILab/XYZ-Aquila-pro
```

XYZ AI Lab — We Build The Minds That Build

XYZ-Aquila-pro

An open-weight thinking model for Deep Search.

Introduction

XYZ-Aquila is a family of open-weight Deep Search agents developed by XYZ AI Lab. XYZ-Aquila-pro is post-trained from Qwen3.5-397B-A17B through a bounded-exploration AI4AI pipeline: humans define the target capability, development evidence, constraints, risk boundaries, and acceptance policy, while AI agents diagnose failures and propose scoped interventions across data, post-training, runtime, context management, tools, evaluation, and infrastructure.

The released checkpoint is a thinking model with Qwen-compatible reasoning and tool-call formats. It is optimized for agentic search, including long-horizon planning, English and Chinese web browsing, multi-source evidence aggregation, source verification, and recovery from failed environment interactions. The open-source AxisAgentic harness provides the concrete search / scrape / python tool implementations, fixed tool contract, replayable context management, and benchmark evaluation workflow; these capabilities are supplied by the surrounding harness rather than by the checkpoint alone.

Benchmark Results

The external benchmark suite was held out from routine AI4AI optimization. Following the technical report, evaluation uses a ReAct-style search harness with web search, webpage extraction, stateful Python execution, and a maximum 256K context. XYZ-Aquila-pro obtains the highest reported score in every column of the evaluated sub-400B open-weight comparison.

The figure provides a visual overview across six agentic search benchmarks. The tables below transpose the comparison: each row is a benchmark and each column is a model within the group.

Small-scale open-weight (<40B)

Open-weight systems with fewer than 40B parameters, including XYZ-Aquila-mini.

Benchmark	XYZ-Aquila-mini	Agents-A1	Nex-N2-mini	apodex-mini	MiroThinker 1.7 mini
BrowseComp	78.8	75.5	74.1	71.5	67.9
BrowseComp-ZH	82.9	--	79.6†	80.6	--
DeepSearchQA	89.5	--	87.2†	82.2	--
GAIA	97.1	96.0	--	--	80.3
LiveBrowseComp	48.7	29.6†	41.4†	32.8†	34.9†
HLE	51.1	47.6	37.1†	46.8	36.4
WideSearch	80.8	--	62.0	--	73.3†

Large-scale open-weight (<400B)

Open-weight systems with fewer than 400B parameters, including XYZ-Aquila-pro.

Benchmark	XYZ-Aquila-pro	Nex-N2-Pro	MiroThinker 1.7	apodex-1.0
BrowseComp	84.8	83.7†	74.0	75.5
BrowseComp-ZH	85.1	79.6†	75.3	82.6
DeepSearchQA	92.5	92.3†	--	84.6
LiveBrowseComp	53.7	50.4†	34.1†	--
HLE	53.3	50.0†	42.9	49.0
WideSearch	81.2	75.6	--	--

XYZ-Aquila-pro vs. Larger-Scale and Closed-Source Models

XYZ-Aquila-pro compared with larger-scale open-weight and closed-source models.

Benchmark	XYZ-Aquila-pro	apodex-h1	DeepSeek-V4- Pro-Max	Kimi-K2.6	Claude Opus 4.7	GPT-5.5 xhigh
BrowseComp	84.8	90.3	83.4	83.2	79.3	84.4
BrowseComp-ZH	85.1	84.1	--	--	--	--
DeepSearchQA	92.5	94.4	--	92.5	89.1	--
LiveBrowseComp	53.7	--	38.3	31.7	--	--
HLE	53.3	60.8	--	55.5	54.7	52.2
WideSearch	81.2	--	--	80.8	--	--

All values are percentages. HLE denotes Humanity's Last Exam. DeepSearchQA uses F1, WideSearch uses Item F1 Max@4, and the remaining benchmarks use accuracy. Rows with no reported result across an entire group are omitted; -- indicates an unreported result within an otherwise populated row. † marks results reproduced under the common evaluation setup; other baseline values come from public reports or benchmark submissions. See the technical report for full provenance and analysis.

Quickstart

SGLang Deployment

Use a recent SGLang release (sglang>=0.5.10). The example below launches an OpenAI-compatible endpoint with Qwen reasoning and tool-call parsers. It uses tensor parallelism across eight GPUs; reduce the context length if the deployment does not have enough memory.

uv pip install "sglang[all]>=0.5.10"

MODEL_PATH=XYZAILab/XYZ-Aquila-pro
SERVED_MODEL=XYZ-Aquila-pro

python -m sglang.launch_server \
  --model-path "${MODEL_PATH}" \
  --served-model-name "${SERVED_MODEL}" \
  --port 8000 \
  --tp-size 8 \
  --mem-fraction-static 0.8 \
  --context-length 262144 \
  --reasoning-parser qwen3 \
  --tool-call-parser qwen3_coder

For XYZ-Aquila-mini, replace MODEL_PATH and SERVED_MODEL with the mini repository and use the tensor-parallel configuration appropriate for your hardware.

Recommended Sampling Config

These are recommended starting values for thinking-mode agentic search. Tune them for the target task and harness.

temperature: 1.0
top_p: 0.95
repetition_penalty: 1.05
chat_template_kwargs:
  enable_thinking: true
  preserve_thinking: true

Keep the input and generated response within the 262,144-token context window.

Citation

@techreport{xyz_aquila_2026,
  title       = {AI4AI at Scale: A Full-Pipeline System for Enhancing LLM Agentic Capabilities},
  author      = {{XYZ Agentic Team}},
  institution = {XYZ AI Lab},
  year        = {2026},
  url         = {https://xyz-lab.ai/blogs/ai4ai-at-scale/assets/bounded-exploration-ai4ai-system-optimization.pdf}
}