Instructions to use tiny-random/lfm3-vl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tiny-random/lfm3-vl with Transformers:

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

pipe = pipeline("image-text-to-text", model="tiny-random/lfm3-vl", 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("tiny-random/lfm3-vl", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tiny-random/lfm3-vl with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tiny-random/lfm3-vl"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tiny-random/lfm3-vl",
		"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/tiny-random/lfm3-vl

SGLang

How to use tiny-random/lfm3-vl 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 "tiny-random/lfm3-vl" \
    --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": "tiny-random/lfm3-vl",
		"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 "tiny-random/lfm3-vl" \
        --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": "tiny-random/lfm3-vl",
		"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 tiny-random/lfm3-vl with Docker Model Runner:
```
docker model run hf.co/tiny-random/lfm3-vl
```

This is a randomly initialized tiny model based on LiquidAI/LFM3-VL-2B for testing and debugging.

Feature	Source	This repo
Checkpoint tensor elements	1.92B BF16	13.81M BF16
Safetensors weight files	3.84 GB (model.safetensors)	27.64 MB (model.safetensors)
Text layers / layer-type cycle	24 / two-conv prefix, then full/conv/sliding/conv	10 / same prefix and two complete cycles
Text hidden width / MLP width	2,048 / 7,680	64 / 128
Text Q heads / KV heads / head dim	16 / 4 / 128	16 / 4 / 32
Vision layers / layer-type cycle	14 / two-conv prefix, then six full/conv pairs	6 / same prefix and two complete full/conv cycles
Vision hidden width / MLP width	1,024 / 2,560	256 / 640
Vision Q heads / KV heads / head dim	16 / 8 / 64	8 / 4 / 32
Patch / temporal patch / spatial merge	16 / 2 / 2	Same
Vision depthwise conv kernel	3 x 3	Same
RoPE	vision axial theta 1e6; text full/sliding dual theta	Same
Projector MLP hidden / downsample factor	4,096 / 2	128 / 2
Sandwich norms / QK RMSNorm / sigmoid head gate	present	Same
Vocabulary rows / embedding tying	128,000 / tied	Same
Text maximum context length	128,000	Same
Stored precision	BF16	Same

Text-backbone widths follow tiny-random/lfm3, which follows tiny-random/lfm2. The pinned upstream implementation is included for Transformers loading. This repo provides a Transformers example with image and video inputs; no serving-backend validation is claimed.

File path	Size
model.safetensors	27.6MB

Example usage

Transformers (✅ verified)

Load the included pinned upstream implementation with trust_remote_code=True. The example sends one image and four predecoded video frames with complete VideoMetadata; the Triton short-conv path requires CUDA.

def run_transformers(model_id: str):
    import numpy as np
    import torch
    from PIL import Image
    from transformers import AutoModelForImageTextToText, AutoProcessor
    from transformers.video_utils import VideoMetadata

    model_id = str(model_id)
    processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
    model = (
        AutoModelForImageTextToText.from_pretrained(
            model_id, trust_remote_code=True, dtype=torch.bfloat16, attn_implementation="sdpa"
        )
        .eval()
        .cuda()
    )
    image = Image.fromarray(np.random.randint(0, 255, (256, 256, 3), dtype=np.uint8), "RGB")
    frames = np.stack(
        [
            np.asarray(Image.new("RGB", (256, 256), color))
            for color in ((220, 30, 30), (30, 220, 30), (30, 30, 220), (220, 220, 30))
        ]
    )
    messages = [
        {
            "role": "user",
            "content": [
                {"type": "image", "image": image},
                {"type": "video", "video": frames},
                {"type": "text", "text": "Describe the image and the video."},
            ],
        }
    ]
    inputs = processor.apply_chat_template(
        messages,
        add_generation_prompt=True,
        tokenize=True,
        return_dict=True,
        return_tensors="pt",
        enable_thinking=False,
        processor_kwargs={"video_metadata": [VideoMetadata(total_num_frames=4, fps=2, frames_indices=[0, 1, 2, 3])]},
    ).to(model.device)
    assert inputs["pixel_values"].shape[0] > 0 and inputs["pixel_values_videos"].shape[0] > 0
    with torch.no_grad():
        logits = model(**inputs).logits
        assert torch.isfinite(logits).all()
        output = model.generate(**inputs, do_sample=False, max_new_tokens=32)
    print(processor.decode(output[0, inputs["input_ids"].shape[1] :], skip_special_tokens=False))


if __name__ == "__main__":
    run_transformers(model_id="tiny-random/lfm3-vl")

Creating this repo

Code: .meta/create_model.py
Model structure: .meta/model.log
Environment: .meta/env.json

Change log

2026-11-01: Initial version. Text backbone matches tiny-random/lfm3's residual/MLP widths (64/128, ten layers with the two-conv prefix and two full/conv/sliding/conv cycles) but keeps the source 4:1 Q/KV ratio with 16 Q heads and at least four KV heads (4) at head dim 32 for TP=4. The vision tower keeps the source 2:1 Q/KV ratio with 8 Q heads / 4 KV heads, which requires width 256 for its derived head dim of 32; it retains the conv/conv prefix, two full/conv cycles, patch 16, temporal patch 2, merge size 2 and axial RoPE.

This repo is generated by GPT-6.1-Sol.