Instructions to use webbrain-one/webbrain-compass-vld-v6-2b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use webbrain-one/webbrain-compass-vld-v6-2b with Transformers:

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

pipe = pipeline("image-text-to-text", model="webbrain-one/webbrain-compass-vld-v6-2b", trust_remote_code=True)
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForImageTextToText
model = AutoModelForImageTextToText.from_pretrained("webbrain-one/webbrain-compass-vld-v6-2b", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use webbrain-one/webbrain-compass-vld-v6-2b 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-vld-v6-2b"
# 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-vld-v6-2b",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/webbrain-one/webbrain-compass-vld-v6-2b

SGLang

How to use webbrain-one/webbrain-compass-vld-v6-2b 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-vld-v6-2b" \
    --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-vld-v6-2b",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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-vld-v6-2b" \
        --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-vld-v6-2b",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use webbrain-one/webbrain-compass-vld-v6-2b with Docker Model Runner:
```
docker model run hf.co/webbrain-one/webbrain-compass-vld-v6-2b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

WebBrain Compass VLD v6 (2B)

WebBrain Compass VLD v6 2B is WebBrain's default multimodal WebGPU in-browser browser agent model, succeeding Compass VLD Tiny v5. It fine-tunes webbrain-one/lfm3-vld-2b-multipass (derived from LiquidAI/LFM3-VL-2B) on a license-clean 70k multimodal browser agent training mix, operating entirely without tool calls in the browser across three core roles:

Tiny Ask & Standalone Chat: Single-turn and multi-turn Q&A from page text/screenshot and offline Wikipedia / Emergency Box passages with byte-exact citation tokens ([WB-W-XXXXXX]). Politely refuses Act/Dev requests in user's language.
Vision Description: WebBrain standard six-section structured descriptions of browser viewports (Page purpose, Visible text, Inputs, State signals, Blockers, Unknowns).
Decisions via Verify Protocol: Fast verify scoring ('Proposed answer: ... Is the proposed answer correct? Answer Yes or No.') evaluated via Yes-minus-No logit margins and linear probe on final hidden state (decision_hidden).

Companion Repositories

webbrain-one/webbrain-compass-vld-v6-2b-onnx: WebGPU ONNX bundle (decoder_q8f16.onnx, decoder_q4f16.onnx with decision_hidden exposed, vision_encoder_fp16.onnx, and trained linear decision probes).
webbrain-one/webbrain-compass-vld-v6-2b-gguf: GGUF exports (BF16, Q8_0, Q4_K_M, and mmproj-F16) for llama.cpp (/v1/chat/completions + /v1/systemone).
webbrain-one/webbrain-compass-vld-v6-2b-lora: LoRA adapter (r=32, alpha=64) and projector weights.

Evaluation Results & Gates

Evaluation Gate	Target	Achieved Result	Status
Vision Suite (Q4_K_M) (E1)	>= 44/100 strict & > 58 (v5 baseline)	62/100 strict (mean rubric: 0.8930, 0 errors, 1.13s latency)	PASS
Vision Suite (Q8_0) (E1)	Reference verification	66/100 strict (mean rubric: 0.8994, 0 errors, 1.50s latency)	PASS
Six-Section Validity (E5)	>= 99.0%	99.00% (495/500 held-out VL-2 val cases valid)	PASS
d1 final_test Raw Verify (E3)	>= 120/126 verify, 24/24 comp, 0/48 false comp, >= 23/24 unknown	122/126 verify, 24/24 comp, 0/48 false comp, 24/24 unknown	PASS
In-Browser Chat Held-Out (E4)	Lang >= 95%, Act >= 95%, Citations 100%, 0 leaks	Lang: 95.95%, Act: 100.0%, Citations: 100.0%, Leaks: 0	PASS
Routing Replay (E2)	Structured >= 95/100, loose > 32/89, 0 length hits	Structured: 96/100, Loose: 33/89, Length hits: 0	PASS

License & Provenance Audit

Base Model: LiquidAI/LFM3-VL-2B (LFM Open License v1.0).
Training Mix: 70,717 train rows, 1,631 val rows across 5 license-audited components:
- vision_vl2 (46,879): CC-BY-4.0 (WebSight, ScreenParse, MiniWoB++, synthetic web renders).
- vision_curated (5,880): Permissive (Orchard MIT, markov-ai Apache-2.0, look-before-you-click MIT).
- decision_d1 (7,236): Synthetic / Internal d1 balanced-V3 browser decision tasks.
- routing_compass (6,950): Commercial manifest (WebBrain traces, Orchard MIT, Reduced-A11y CC BY 4.0, markov-ai Apache-2.0, Mind2Web CC BY 4.0).
- chat_inbrowser (3,822): CC-BY-4.0 / CC-BY-SA (Multilingual page QA, Turkish >= 20%, Act refusals, offline RAG, query translations).
Strict Exclusions: WebChain (strictly excluded), WebLINX (strictly excluded), Compass v3 (strictly excluded).