Instructions to use webbrain-one/webbrain-compass-tiny-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use webbrain-one/webbrain-compass-tiny-v2 with Transformers:

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

pipe = pipeline("text-generation", model="webbrain-one/webbrain-compass-tiny-v2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("webbrain-one/webbrain-compass-tiny-v2")
model = AutoModelForCausalLM.from_pretrained("webbrain-one/webbrain-compass-tiny-v2", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use webbrain-one/webbrain-compass-tiny-v2 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-v2"
# 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-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/webbrain-one/webbrain-compass-tiny-v2

SGLang

How to use webbrain-one/webbrain-compass-tiny-v2 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-v2" \
    --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-v2",
		"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-v2" \
        --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-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use webbrain-one/webbrain-compass-tiny-v2 with Docker Model Runner:
```
docker model run hf.co/webbrain-one/webbrain-compass-tiny-v2
```

webbrain-compass-tiny-v2 / provenance /merged-bf16-results.md

webbrain-one

Upload private Compass v2 model batch 2/3

a9d1f19 verified 2 months ago

preview code

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2.98 kB

Compass v2 merged BF16 v4 — result and release boundary

Completed 2026-09-10 15:38 Istanbul, independently audited at 15:39. This report does not modify the original native-adapter benchmark or its scores.

Metric	Native final adapter	Merged BF16 candidate	Approved gate
Structured first-turn calls	93/100	94/100	>=93/100
First-turn HTTP/runner errors	0	0	0
Strict tool and arguments	15/89	16/89	>=15/89
Loose/name-only-inclusive match	43/89	41/89	>=43/89 — FAILED
Known anti-pattern	1	1	<=1
Scenario HTTP/runner errors	1 timeout	0	<=1

The candidate passes the mathematical merge, independent weight, native runtime buffer, finite-weight, save/reload identity, eight-probe FP32 and BF16 greedy/logit and KV-cache checks. It does NOT pass the complete v4 release contract: loose matching drops by two cases, from 48.3% to 46.1%. Strict matching rises by one case, from 16.9% to 18.0%. These small-suite results are not statistical evidence of broad task performance; no browser actions were executed.

One raw response contains malformed XML (parser reports an invalid token). This is separately disclosed from the zero HTTP/runner-error count; it was not repaired or removed from scoring. There were no length-limited responses. All 194 raw responses and all 100 first-turn / 100 scenario result files are preserved. Eleven out-of-scope scenarios remain skipped, leaving 89 scored. All 193 frozen input/code hashes and every recorded scenario reference field match the original native-adapter run.

First-turn latency sum: 148,464 ms. Scenario latency sum: 175,697 ms. The run no longer includes the prior 300-second timeout; do not present the elapsed-time difference as a controlled throughput improvement.

Candidate: E:\webbrain-curated-v2-sources\work\curated-v2\compass-v2-model-release-v1\compass-v2-bf16-v4_2-805c995d3797b8a3

Benchmark: E:\webbrain-curated-v2-sources\work\curated-v2\compass-v2-model-release-v1\compass-v2-bf16-benchmark-v4-805c995d3797b8a3

Candidate verification SHA256: a797ba7921c263142d3c0a43204f188e1424b9c51a95639e2682809415564fce.
Benchmark completion SHA256: 10039496a9539a2b5aea8a176ce5e87761352da806af2192e702d9420f3b1130.
Evidence audit SHA256: 7231b69d180c74bc3f48875c9beb2fc5707628c322d8a79061f8369c0f59de96.

No model was uploaded and no ONNX export was started. The conditional packager was not run. The follow-up is paused pending explicit direction. Do not lower the failed gate, selectively rerun cases, revise expected answers, retrain on this benchmark or call the candidate fully v4-release-approved.

At 15:41 the exact completed benchmark server was stopped after an idle health, command-line/ancestry/listener and all-194-responses check. Other servers were not touched. Shutdown provenance is in server-stop-preflight.json and server-stopped.json under the benchmark root. All failures and originals remain.