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
```

lfm3-vl / .meta /create_model.py

yujiepan

Upload folder using huggingface_hub

bd2c39a verified 4 days ago

Raw History Blame Contribute Delete

3.63 kB

def create_model(source_model_id: str, source_revision: str, save_folder: str) -> str:
    import json
    from pathlib import Path

    import torch
    from huggingface_hub import hf_hub_download
    from transformers import AutoConfig, AutoModelForImageTextToText, set_seed

    folder = Path(save_folder)
    folder.mkdir(parents=True, exist_ok=True)
    # LFM3-VL is not registered in the installed Transformers release; ship its pinned remote code (text backbone,
    # vision tower and the image/video processors) so the checkpoint loads without a newer package.
    for name in (
        "configuration_lfm3.py",
        "configuration_lfm3_vl.py",
        "modeling_lfm3.py",
        "modeling_lfm3_vl.py",
        "lfm3_conv.py",
        "processing_lfm3_vl.py",
        "image_processing_lfm3_vl.py",
        "video_processing_lfm3_vl.py",
        "chat_template.jinja",
        "generation_config.json",
        "processor_config.json",
        "tokenizer.json",
        "tokenizer_config.json",
        "LICENSE",
    ):
        (folder / name).write_bytes(Path(hf_hub_download(source_model_id, name, revision=source_revision)).read_bytes())

    config = json.loads(Path(hf_hub_download(source_model_id, "config.json", revision=source_revision)).read_text())
    # Text backbone: residual/MLP widths 64/128 like tiny-random/lfm3, with at least four KV heads for TP=4 and
    # the source 4:1 Q/KV ratio (16 Q heads, 4 KV heads, rectangular projections at head dim 32), the two-conv
    # prefix plus two complete full/conv/sliding/conv cycles.
    text_config = config["text_config"]
    text_config.update(
        hidden_size=64,
        intermediate_size=128,
        num_hidden_layers=10,
        num_attention_heads=16,
        num_key_value_heads=4,
        head_dim=32,
        max_position_embeddings=128000,
    )
    text_config["layer_types"] = text_config["layer_types"][:10]
    # Vision tower: at least four KV heads with the source 2:1 Q/KV ratio (8 Q heads, 4 KV heads). Its head dim is
    # derived as hidden // num_heads, so width 256 keeps head dim 32 and axial RoPE; patch 16 / temporal patch 2 /
    # spatial merge 2. Six layers retain the conv/conv prefix and two full/conv cycles.
    vision_config = config["vision_config"]
    vision_config.update(
        hidden_size=256,
        intermediate_size=640,
        num_hidden_layers=6,
        num_attention_heads=8,
        num_key_value_heads=4,
    )
    vision_config["layer_types"] = ["conv", "conv", "full_attention", "conv", "full_attention", "conv"]
    # Projector: LayerNorm + GELU MLP merging 2x2 vision blocks; keep bias and the downsample contract.
    config["projector_hidden_size"] = 128
    (folder / "config.json").write_text(json.dumps(config, indent=2) + "\n")

    set_seed(42)
    model = AutoModelForImageTextToText.from_config(
        AutoConfig.from_pretrained(folder, trust_remote_code=True), trust_remote_code=True, dtype=torch.bfloat16
    )
    model.save_pretrained(folder)
    # `save_pretrained()` rewrites these two files with this environment's versions; restore the pinned source bytes
    # plus the deliberate config edits so only the intended fields differ from the source.
    (folder / "config.json").write_text(json.dumps(config, indent=2) + "\n")
    (folder / "generation_config.json").write_bytes(
        Path(hf_hub_download(source_model_id, "generation_config.json", revision=source_revision)).read_bytes()
    )
    return str(model)


create_model(
    source_model_id="LiquidAI/LFM3-VL-2B",
    source_revision="ba44bd51db20a2753f28c273a1571ccaa210d954",
    save_folder="/tmp/lfm3-vl",
)

	def create_model(source_model_id: str, source_revision: str, save_folder: str) -> str:
	import json
	from pathlib import Path

	import torch
	from huggingface_hub import hf_hub_download
	from transformers import AutoConfig, AutoModelForImageTextToText, set_seed

	folder = Path(save_folder)
	folder.mkdir(parents=True, exist_ok=True)
	# LFM3-VL is not registered in the installed Transformers release; ship its pinned remote code (text backbone,
	# vision tower and the image/video processors) so the checkpoint loads without a newer package.
	for name in (
	"configuration_lfm3.py",
	"configuration_lfm3_vl.py",
	"modeling_lfm3.py",
	"modeling_lfm3_vl.py",
	"lfm3_conv.py",
	"processing_lfm3_vl.py",
	"image_processing_lfm3_vl.py",
	"video_processing_lfm3_vl.py",
	"chat_template.jinja",
	"generation_config.json",
	"processor_config.json",
	"tokenizer.json",
	"tokenizer_config.json",
	"LICENSE",
	):
	(folder / name).write_bytes(Path(hf_hub_download(source_model_id, name, revision=source_revision)).read_bytes())

	config = json.loads(Path(hf_hub_download(source_model_id, "config.json", revision=source_revision)).read_text())
	# Text backbone: residual/MLP widths 64/128 like tiny-random/lfm3, with at least four KV heads for TP=4 and
	# the source 4:1 Q/KV ratio (16 Q heads, 4 KV heads, rectangular projections at head dim 32), the two-conv
	# prefix plus two complete full/conv/sliding/conv cycles.
	text_config = config["text_config"]
	text_config.update(
	hidden_size=64,
	intermediate_size=128,
	num_hidden_layers=10,
	num_attention_heads=16,
	num_key_value_heads=4,
	head_dim=32,
	max_position_embeddings=128000,
	)
	text_config["layer_types"] = text_config["layer_types"][:10]
	# Vision tower: at least four KV heads with the source 2:1 Q/KV ratio (8 Q heads, 4 KV heads). Its head dim is
	# derived as hidden // num_heads, so width 256 keeps head dim 32 and axial RoPE; patch 16 / temporal patch 2 /
	# spatial merge 2. Six layers retain the conv/conv prefix and two full/conv cycles.
	vision_config = config["vision_config"]
	vision_config.update(
	hidden_size=256,
	intermediate_size=640,
	num_hidden_layers=6,
	num_attention_heads=8,
	num_key_value_heads=4,
	)
	vision_config["layer_types"] = ["conv", "conv", "full_attention", "conv", "full_attention", "conv"]
	# Projector: LayerNorm + GELU MLP merging 2x2 vision blocks; keep bias and the downsample contract.
	config["projector_hidden_size"] = 128
	(folder / "config.json").write_text(json.dumps(config, indent=2) + "\n")

	set_seed(42)
	model = AutoModelForImageTextToText.from_config(
	AutoConfig.from_pretrained(folder, trust_remote_code=True), trust_remote_code=True, dtype=torch.bfloat16
	)
	model.save_pretrained(folder)
	# `save_pretrained()` rewrites these two files with this environment's versions; restore the pinned source bytes
	# plus the deliberate config edits so only the intended fields differ from the source.
	(folder / "config.json").write_text(json.dumps(config, indent=2) + "\n")
	(folder / "generation_config.json").write_bytes(
	Path(hf_hub_download(source_model_id, "generation_config.json", revision=source_revision)).read_bytes()
	)
	return str(model)


	create_model(
	source_model_id="LiquidAI/LFM3-VL-2B",
	source_revision="ba44bd51db20a2753f28c273a1571ccaa210d954",
	save_folder="/tmp/lfm3-vl",
	)