Instructions to use webbrain-one/lfm3-vld-2b-multipass with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use webbrain-one/lfm3-vld-2b-multipass with Transformers:

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

pipe = pipeline("image-text-to-text", model="webbrain-one/lfm3-vld-2b-multipass", 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/lfm3-vld-2b-multipass", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use webbrain-one/lfm3-vld-2b-multipass with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "webbrain-one/lfm3-vld-2b-multipass"
# 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/lfm3-vld-2b-multipass",
		"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/lfm3-vld-2b-multipass

SGLang

How to use webbrain-one/lfm3-vld-2b-multipass 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/lfm3-vld-2b-multipass" \
    --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/lfm3-vld-2b-multipass",
		"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/lfm3-vld-2b-multipass" \
        --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/lfm3-vld-2b-multipass",
		"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/lfm3-vld-2b-multipass with Docker Model Runner:
```
docker model run hf.co/webbrain-one/lfm3-vld-2b-multipass
```

LFM3-VL-2B-VLD Multipass

LiquidAI/LFM3-VL-2B adapted with WebBrain's multipass VL-D (verify Yes/No per option + readout option code) decision protocol, distilled from LiquidAI/d1-2B on WebBrain multimodal browser workflow screenshots — without full VL/Compass fine-tuning (vision tower frozen, language backbone adapted via LoRA and merged to BF16).

Companion Repositories

webbrain-one/lfm3-vld-2b-multipass-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/lfm3-vld-2b-multipass-gguf: GGUF exports (BF16, Q8_0, Q4_K_M, and mmproj-F16) for llama.cpp (/v1/chat/completions + /v1/systemone).
webbrain-one/lfm3-vld-2b-multipass-lora: LoRA adapter (r=32, alpha=64) and projector weights.

Results on d1 Balanced Browser Decision Benchmark (`final_test`, 126 Categorical Questions)

Model / Protocol	Correct	Completions Found	False Completions	Unknown Recognized	Score MAE
LFM3-VLD-2B (`probe` / `probe-cs`)	126/126 (100.0%)	24/24	0/48	24/24	9.56e-8
LFM3-VLD-2B (`verify` zero-shot)	126/126 (100.0%)	24/24	0/48	24/24	4.11e-5
LFM3-VLD-2B (`readout`)	125/126 (99.2%)	24/24	0/48	24/24	0.0189
Base `LiquidAI/LFM3-VL-2B` (`verify` zero-shot)	106/126 (84.1%)	24/24	0/48	4/24	0.0004
Base `LiquidAI/LFM3-VL-2B` (`readout`)	102/126 (81.0%)	24/24	1/48	0/24	0.1106