Instructions to use webbrain-one/webbrain-compass-tiny-xs-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use webbrain-one/webbrain-compass-tiny-xs-v3 with Transformers:

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

pipe = pipeline("text-generation", model="webbrain-one/webbrain-compass-tiny-xs-v3", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("webbrain-one/webbrain-compass-tiny-xs-v3", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use webbrain-one/webbrain-compass-tiny-xs-v3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "webbrain-one/webbrain-compass-tiny-xs-v3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "webbrain-one/webbrain-compass-tiny-xs-v3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/webbrain-one/webbrain-compass-tiny-xs-v3

SGLang

How to use webbrain-one/webbrain-compass-tiny-xs-v3 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 "webbrain-one/webbrain-compass-tiny-xs-v3" \
    --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": "webbrain-one/webbrain-compass-tiny-xs-v3",
		"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 "webbrain-one/webbrain-compass-tiny-xs-v3" \
        --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": "webbrain-one/webbrain-compass-tiny-xs-v3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use webbrain-one/webbrain-compass-tiny-xs-v3 with Docker Model Runner:
```
docker model run hf.co/webbrain-one/webbrain-compass-tiny-xs-v3
```

webbrain-compass-tiny-xs-v3 / README.md

webbrain-one

WebBrain Compass Tiny XS v3 release

eedb5be 12 days ago

preview code

Raw History Blame Contribute Delete

4.69 kB

	---
	license: other
	license_name: noncommercial-research-only
	license_link: https://huggingface.co/webbrain-one/webbrain-compass-tiny-xs-v3/blob/main/README.md#license
	base_model: XHToken/Spark-X2.5-1.7B
	library_name: transformers
	pipeline_tag: text-generation
	tags:
	- webbrain
	- tool-calling
	- spark2_5
	- lora
	---

	# WebBrain Compass Tiny XS v3

	WebBrain Compass Tiny XS v3 is a compact language model optimized for low-latency decision making, structured tool use, and in-browser agentic execution inside the WebBrain runtime.

	Fine-tuned from Spark-X2.5-1.7B (~1.7B parameters) (`XHToken/Spark-X2.5-1.7B` at `14d6e83c13c7add2b62a7c39b2131f4ed1cddcf8`), this is the BF16 reference model with merged weights, accompanied by the original LoRA adapter under `adapter/`.

	It unifies three core WebBrain behavioral modes:

	1. Ask & Clarify — Answer directly or request missing information when execution is underspecified or unnecessary.
	2. Direct Compact Tool Execution — Select exact browser tools and generate grounded arguments for low-latency accessibility-tree actions.
	3. Safe Escalation & Abstention — Refuse, pause, or defer to higher execution tiers when actions lack adequate grounding or violate safety boundaries.

	> Note: Display/repository name is WebBrain Compass Tiny XS v3. This is a metadata-only rename preserving tested weight/code revision `main`. For native in-browser WebGPU execution, see the companion ONNX package: [`webbrain-one/webbrain-compass-tiny-xs-v3-onnx`](https://huggingface.co/webbrain-one/webbrain-compass-tiny-xs-v3-onnx).

	---

	## Role in WebBrain

	```text
	User request
	│
	▼
	WebBrain observation & policy layer
	│
	▼
	WebBrain Compass Tiny XS v3 (BF16 Reference)
	├─ Clarify or answer directly
	├─ Emit grounded browser tool call
	└─ Abstain / safely escalate when execution is ungrounded
	```

	The outer WebBrain runtime enforces tool-schema validation, evidence and parameter grounding, destination URL verification, browser-state freshness, and sandboxed security policies. Model output is never proof that an external action succeeded.

	---

	## Intended Capabilities

	- Browser Action Selection: Grounded accessibility-tree interactions and structured function calling.
	- Reference Decision Making: Fast, high-fidelity local inference for compact browser actions.
	- Unified Ask and Compact Modes: Asking for missing information before acting, answering direct questions, or choosing tools.
	- Safe Refusal & Escalation: Refusing unsupported actions or escalating when Compact execution lacks evidence, avoiding invented URLs or fabricated success states.

	---

	## Quickstart & Loading

	You can load and run WebBrain Compass Tiny XS v3 directly with Hugging Face `transformers`:

	```python
	import torch
	from transformers import AutoTokenizer, AutoModelForCausalLM

	repo = "webbrain-one/webbrain-compass-tiny-xs-v3"
	revision = "main" # Tested weight/code revision

	tokenizer = AutoTokenizer.from_pretrained(repo, revision=revision)
	model = AutoModelForCausalLM.from_pretrained(
	repo,
	revision=revision,
	trust_remote_code=True,
	dtype=torch.bfloat16,
	device_map="auto"
	)

	# Example structured tool use
	messages = [
	{"role": "user", "content": "Click the sign-in button on the page"}
	]

	inputs = tokenizer.apply_chat_template(
	messages,
	tools=tools,
	enable_thinking=False,
	add_generation_prompt=True,
	return_tensors="pt",
	return_dict=True
	).to(model.device)

	outputs = model.generate(**inputs, max_new_tokens=512, do_sample=False)
	response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=False)
	```

	### Tool Calling Format
	Use native structured `tool_calls` with dictionary arguments; do not reuse MiniCPM's template or tool serialization. Spark emits `<tool_call>name` with `<arg_key>`/`<arg_value>` fields. For long contexts in production, use a Spark-compatible serving runtime.

	---

	## Technical Specifications

	## Fixed BF16 Routing Results (2026-09-26)

	\| Metric \| BF16 Routing Score \|
	\|---\|---:\|
	\| First-turn structured calls \| 97/100 \|
	\| Strict exact action \| 9/89 \|
	\| Loose tool-family match \| 39/89 \|

	Evaluated across 100 first turns and 89 scored fixed-history scenarios (11 predetermined skips). Retains five generation timeouts and five discouraged actions in the denominators without cherry-picking.

	---

	## License

	Noncommercial research only. This model and its fine-tuned weights are for noncommercial research purposes only. The upstream Spark model/code license is included in `LICENSE`; it does not change this release's usage restriction.