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 AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("webbrain-one/webbrain-compass-vld-v6-2b", trust_remote_code=True)
model = AutoModelForMultimodalLM.from_pretrained("webbrain-one/webbrain-compass-vld-v6-2b", trust_remote_code=True, device_map="auto")
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?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

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

webbrain-one

Upload webbrain-one/webbrain-compass-vld-v6-2b (Compass VLD v6)

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---
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:

1. **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.
2. **Vision Description**: WebBrain standard six-section structured descriptions of browser viewports (Page purpose, Visible text, Inputs, State signals, Blockers, Unknowns).
3. **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`](https://huggingface.co/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`](https://huggingface.co/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`](https://huggingface.co/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).

	---
	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:

	1. 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.
	2. Vision Description: WebBrain standard six-section structured descriptions of browser viewports (Page purpose, Visible text, Inputs, State signals, Blockers, Unknowns).
	3. 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`](https://huggingface.co/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`](https://huggingface.co/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`](https://huggingface.co/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).