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)

# 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=40)
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
```

LFM2.5-VL-3B

File size: 5,095 Bytes

3463bfe

{
  "architectures": [
    "Lfm2VlForConditionalGeneration"
  ],
  "auto_map": {},
  "bos_token_id": 124894,
  "do_image_splitting": true,
  "do_resize": true,
  "downsample_factor": 2,
  "dtype": "bfloat16",
  "encoder_patch_size": 16,
  "eos_token_id": 124900,
  "freeze_language_model": false,
  "freeze_multi_modal_projector": false,
  "freeze_vision_tower": false,
  "image_token_id": 124907,
  "keep_trainable_parameters_fp32": true,
  "language_model_lr_multiplier": 1.0,
  "lfm2_attention_backend": "flash_varlen",
  "lfm2_attention_fusion": "fused_linear",
  "lfm2_flash_varlen_blhd_fastpath": false,
  "lfm2_frozen_input_grad_only_linear": true,
  "lfm2_mlp_fusion": "triton_swiglu",
  "lfm2_rmsnorm_fusion": "none",
  "lfm2_short_conv_frozen_recompute_in_proj": 0,
  "lfm2_torch_compile_clone_outputs": false,
  "lfm2_torch_compile_disable_cudagraphs": true,
  "lfm2_torch_compile_dynamic": true,
  "lfm2_torch_compile_fullgraph": false,
  "lfm2_torch_compile_layers": "none",
  "lfm2_torch_compile_mlp": false,
  "lfm2_torch_compile_mode": "reduce-overhead",
  "lfm2_vl_direct_image_merge_by_shape": true,
  "lfm2_vl_forward_dtype": "bf16",
  "lfm2_vl_frozen_vision_chunk_size": 2048,
  "lfm2_vl_grouped_image_projector": true,
  "lfm2_vl_grouped_vision_by_patch_count": false,
  "lfm2_vl_grouped_vision_by_shape": true,
  "lfm2_vl_inplace_image_merge": true,
  "lfm2_vl_siglip_packed_vision": true,
  "lfm2_vl_torch_compile_vision_encoder": true,
  "lfm2_vl_torch_compile_vision_encoder_mode": "whole",
  "lm_head_loss_mask_only": true,
  "max_image_tokens": 256,
  "max_num_patches": 1024,
  "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,
  "projector_hidden_act": "gelu",
  "projector_hidden_size": 2048,
  "projector_lr_multiplier": 1.0,
  "projector_use_layernorm": false,
  "siglip2_layernorm_fusion": "liger",
  "text_config": {
    "block__name_mlp": "parallel_mlp_merged",
    "block_auto_adjust_ff_dim": false,
    "block_dim": 2048,
    "block_ffn_dim_multiplier": 1.0,
    "block_ffn_te_autocast": false,
    "block_ffn_use_quantized_params": false,
    "block_mlp_init_scale": 1.0,
    "block_multiple_of": 256,
    "block_norm_eps": 1e-05,
    "block_out_init_scale": 1.0,
    "block_use_swiglu": true,
    "block_use_xavier_init": true,
    "bos_token_id": 124894,
    "conv_L_cache": 3,
    "conv_bias": false,
    "conv_dim": 2048,
    "conv_use_xavier_init": true,
    "dtype": "bfloat16",
    "eos_token_id": 124900,
    "full_attn_idxs": null,
    "hidden_size": 2048,
    "initializer_range": 0.02,
    "intermediate_size": 10752,
    "layer_types": [
      "conv",
      "conv",
      "full_attention",
      "conv",
      "conv",
      "full_attention",
      "conv",
      "conv",
      "conv",
      "full_attention",
      "conv",
      "conv",
      "conv",
      "full_attention",
      "conv",
      "conv",
      "conv",
      "full_attention",
      "conv",
      "conv",
      "conv",
      "full_attention",
      "conv",
      "conv",
      "full_attention",
      "conv",
      "conv",
      "full_attention",
      "conv",
      "conv"
    ],
    "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",
      "cuda_sync_points": [],
      "downsample": 2,
      "encoder_load_balance": false,
      "hidden_dim": 2048,
      "hidden_state_index": -1,
      "layer_norm": false,
      "max_batch_size": 1024,
      "pretrained_model_name_or_path": "google/siglip2-so400m-patch16-naflex",
      "set_activation_checkpointing": false,
      "use_image_special_tokens": true,
      "use_pooling_head": false,
      "use_slice_special_tokens": false,
      "use_torch_pixel_unshuffle": false
    },
    "model_type": "lfm2",
    "norm_eps": 1e-05,
    "num_attention_heads": 32,
    "num_heads": 32,
    "num_hidden_layers": 30,
    "num_key_value_heads": 8,
    "output_softcap": 0.0,
    "pad_token_id": 124893,
    "rope_parameters": {
      "rope_theta": 1000000.0,
      "rope_type": "default"
    },
    "tie_word_embeddings": true,
    "use_cache": true,
    "use_pos_enc": true,
    "vocab_size": 128000,
    "architectures": [
      "Lfm2ForCausalLM"
    ],
    "tie_embedding": true
  },
  "tie_word_embeddings": true,
  "tile_size": 512,
  "transformers_version": "5.8.1",
  "use_image_special_tokens": true,
  "use_thumbnail": true,
  "vision_config": {
    "attention_dropout": 0.0,
    "dtype": "bfloat16",
    "hidden_act": "gelu_pytorch_tanh",
    "hidden_size": 1152,
    "intermediate_size": 4304,
    "layer_norm_eps": 1e-06,
    "model_type": "siglip2_vision_model",
    "num_attention_heads": 16,
    "num_channels": 3,
    "num_hidden_layers": 27,
    "num_patches": 256,
    "patch_size": 16,
    "vision_use_head": false
  },
  "vision_tower_lr_multiplier": 1.0
}