Instructions to use LiquidAI/LFM2.5-VL-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LiquidAI/LFM2.5-VL-3B with Transformers:

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

pipe = pipeline("image-text-to-text", model="LiquidAI/LFM2.5-VL-3B", 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("LiquidAI/LFM2.5-VL-3B", trust_remote_code=True)
model = AutoModelForMultimodalLM.from_pretrained("LiquidAI/LFM2.5-VL-3B", 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 LiquidAI/LFM2.5-VL-3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LiquidAI/LFM2.5-VL-3B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LiquidAI/LFM2.5-VL-3B",
		"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/LiquidAI/LFM2.5-VL-3B

SGLang

How to use LiquidAI/LFM2.5-VL-3B 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 "LiquidAI/LFM2.5-VL-3B" \
    --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": "LiquidAI/LFM2.5-VL-3B",
		"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 "LiquidAI/LFM2.5-VL-3B" \
        --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": "LiquidAI/LFM2.5-VL-3B",
		"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 LiquidAI/LFM2.5-VL-3B with Docker Model Runner:
```
docker model run hf.co/LiquidAI/LFM2.5-VL-3B
```

samuelstevens commited on Aug 31

Commit

a3af579

verified ·

1 Parent(s): 5a414ea

Update configuration

Browse files

Files changed (2) hide show

config.json +2 -2
processor_config.json +1 -1

config.json CHANGED Viewed

@@ -46,7 +46,7 @@
   "max_pixels_tolerance": 2.0,
   "max_tiles": 10,
   "min_image_tokens": 64,
-  "min_tiles": 1,
   "model_type": "lfm2_vl",
   "pad_token_id": 124893,
   "projector_bias": true,
@@ -114,7 +114,7 @@
     "lfm2_attention_backend": "flash_varlen",
     "lfm2_flash_varlen_blhd_fastpath": false,
     "lfm2_frozen_input_grad_only_linear": true,
-    "max_position_embeddings": 128000,
     "mm_config_image_embedder": {
       "activation_checkpointing_layer_stride": 0,
       "attn_implementation": "sdpa",

   "max_pixels_tolerance": 2.0,
   "max_tiles": 10,
   "min_image_tokens": 64,
+  "min_tiles": 2,
   "model_type": "lfm2_vl",
   "pad_token_id": 124893,
   "projector_bias": true,
     "lfm2_attention_backend": "flash_varlen",
     "lfm2_flash_varlen_blhd_fastpath": false,
     "lfm2_frozen_input_grad_only_linear": true,
+    "max_position_embeddings": 32768,
     "mm_config_image_embedder": {
       "activation_checkpointing_layer_stride": 0,
       "attn_implementation": "sdpa",

processor_config.json CHANGED Viewed

@@ -24,7 +24,7 @@
     "max_pixels_tolerance": 2.0,
     "max_tiles": 10,
     "min_image_tokens": 64,
-    "min_tiles": 1,
     "resample": 3,
     "rescale_factor": 0.00392156862745098,
     "return_row_col_info": true,

     "max_pixels_tolerance": 2.0,
     "max_tiles": 10,
     "min_image_tokens": 64,
+    "min_tiles": 2,
     "resample": 3,
     "rescale_factor": 0.00392156862745098,
     "return_row_col_info": true,