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)

# 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
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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:

Ask & Clarify — Answer directly or request missing information when execution is underspecified or unnecessary.
Direct Compact Tool Execution — Select exact browser tools and generate grounded arguments for low-latency accessibility-tree actions.
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.

Role in WebBrain

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:

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.

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Safetensors

Model size

2B params

Tensor type

BF16

Model tree for webbrain-one/webbrain-compass-tiny-xs-v3

Base model

XHToken/Spark-X2.5-1.7B-Base

Finetuned

XHToken/Spark-X2.5-1.7B

Adapter

(6)

this model

Quantizations

1 model