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

File size: 4,691 Bytes

eedb5be

---
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.