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

Libraries

How to use webbrain-one/lfm3-vld-2b 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", 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", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use webbrain-one/lfm3-vld-2b 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"
# 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",
		"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

SGLang

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

lfm3-vld-2b / README.md

webbrain-one

Update README.md

fbe2e60 verified about 16 hours ago

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metadata

license: other
license_name: lfm1.0
base_model: LiquidAI/d1-2B
pipeline_tag: image-text-to-text
library_name: transformers
tags:
  - webbrain
  - liquid
  - lfm3
  - lfm3-vl
  - browser-agent
  - decision-model
  - webbrain-vld
  - vldr
  - pegasus-method
  - custom_code

LFM3-VLD-2B

LFM3-VLD-2B (Restored Language Head) is a hybrid multimodal browser decision model built using the Pegasus method (inspired by AveLabs/Qwen3.8-27B-Perplexing-Pegasus).

It combines:

Decision Backbone: The complete 24-layer hybrid architecture (Conv + GQA attention + Feed-Forward), vision tower, multimodal projector, and embeddings from LiquidAI/d1-2B (trained on browser actions, verification, and decision trajectories).
Restored Language Head: The original uncorrupted generative language head (lm_head.weight) from LiquidAI/LFM3-VL-2B, untied from token embeddings (tie_word_embeddings: false).

The Problem Solved

When models like LiquidAI/d1-2B undergo full fine-tuning on decision tasks with tied word embeddings, the language head weights drift away from fluent language generation, causing token repetition and degraded prose in standard causal LM generation. By untying the embeddings and restoring the original LFM3-VL-2B language head onto the decision-trained backbone, LFM3-VL-2B-VLDR achieves:

Flawless open-ended multimodal chat & text generation (no repetition artifacts).
Preserved decision separability: Hidden states retain deep decision knowledge, reaching 91.27% accuracy with 0% false completions on the balanced browser decision benchmark.

Benchmark Results (d1 Balanced Decision Benchmark — `final_test`, 126 Categorical Decisions)

Model / Method	Accuracy	Completion Found	False Completions	Unknown Recognized
LFM3-VLD-2B (Decision Probe)	115/126 (91.3%)	24/24 (100%)	0/48 (0%)	24/24 (100%)
LFM3-VLD-2B (`verify` Zero-Shot)	85/126 (67.5%)	22/24 (91.7%)	0/48 (0%)	0/24 (0%)
LFM3-VL-2B-VLD (LoRA Distillation)	126/126 (100.0%)	24/24 (100%)	0/48 (0%)	24/24 (100%)
Base `LiquidAI/LFM3-VL-2B` (`verify` Zero-Shot)	106/126 (84.1%)	24/24 (100%)	0/48 (0%)	4/24 (16.7%)

Companion Repositories

webbrain-one/lfm3-vl-2b-vldr-gguf: GGUF quantizations (BF16, Q8_0, Q4_K_M, mmproj-F16) for llama.cpp (/v1/chat/completions + /v1/systemone).
webbrain-one/lfm3-vl-2b-vldr-onnx: WebGPU-ready ONNX bundle (decoder_q8f16, decoder_q4f16, vision_encoder_fp16, and decision probe JS runtime).

Quickstart

import torch
from transformers import AutoModelForImageTextToText, AutoProcessor

model_id = "webbrain-one/lfm3-vl-2b-vldr"
processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForImageTextToText.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)

messages = [
    {"role": "user", "content": "What is the capital of France?"}
]
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(text=text, return_tensors="pt").to(model.device)

with torch.no_grad():
    out = model.generate(**inputs, max_new_tokens=64)
print(processor.decode(out[0], skip_special_tokens=True))
# The capital of France is Paris.

LFM3-VLD-2B

The Problem Solved

Benchmark Results (d1 Balanced Decision Benchmark — final_test, 126 Categorical Decisions)

Companion Repositories

Quickstart

Benchmark Results (d1 Balanced Decision Benchmark — `final_test`, 126 Categorical Decisions)