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
```

webbrain-compass-vld-v6-2b / README.md

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metadata

license: other
license_name: lfm1.0
base_model: LiquidAI/LFM3-VL-2B
pipeline_tag: image-text-to-text
library_name: transformers
tags:
  - webbrain
  - compass-v6
  - liquid
  - lfm3
  - lfm3-vl
  - browser-agent
  - decision-model
  - webbrain-vld
  - custom_code

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).