LFM2‑VL-450M

LFM2‑VL is Liquid AI's first series of multimodal models, designed to process text and images with variable resolutions. Built on the LFM2 backbone, it is optimized for low-latency and edge AI applications.

We're releasing the weights of two post-trained checkpoints with 450M (for highly constrained devices) and 1.6B (more capable yet still lightweight) parameters.

• 2× faster inference speed on GPUs compared to existing VLMs while maintaining competitive accuracy
• Flexible architecture with user-tunable speed-quality tradeoffs at inference time
• Native resolution processing up to 512×512 with intelligent patch-based handling for larger images, avoiding upscaling and distortion

Find more about our vision-language model in the LFM2-VL post and its language backbone in the LFM2 blog post.

📄 Model details

Due to their small size, we recommend fine-tuning LFM2-VL models on narrow use cases to maximize performance. They were trained for instruction following and lightweight agentic flows. Not intended for safety‑critical decisions.

Supported languages: English

Generation parameters: We recommend the following parameters:

• Text: temperature=0.1, min_p=0.15, repetition_penalty=1.05
• Vision: min_image_tokens=64 max_image_tokens=256, do_image_splitting=True

Chat template: LFM2-VL uses a ChatML-like chat template as follows:

<|startoftext|><|im_start|>system
You are a helpful multimodal assistant by Liquid AI.<|im_end|>
<|im_start|>user
<image>Describe this image.<|im_end|>
<|im_start|>assistant
This image shows a Caenorhabditis elegans (C. elegans) nematode.<|im_end|>

Images are referenced with a sentinel (<image>), which is automatically replaced with the image tokens by the processor.

You can apply it using the dedicated .apply_chat_template() function from Hugging Face transformers.

Architecture

• Hybrid backbone: Language model tower (LFM2-1.2B or LFM2-350M) paired with SigLIP2 NaFlex vision encoders (400M shape-optimized or 86M base variant)
• Native resolution processing: Handles images up to 512×512 pixels without upscaling and preserves non-standard aspect ratios without distortion
• Tiling strategy: Splits large images into non-overlapping 512×512 patches and includes thumbnail encoding for global context (in 1.6B model)
• Efficient token mapping: 2-layer MLP connector with pixel unshuffle reduces image tokens (e.g., 256×384 image → 96 tokens, 1000×3000 → 1,020 tokens)
• Inference-time flexibility: User-tunable maximum image tokens and patch count for speed/quality tradeoff without retraining

Training approach

• Builds on the LFM2 base model with joint mid-training that fuses vision and language capabilities using a gradually adjusted text-to-image ratio
• Applies joint SFT with emphasis on image understanding and vision tasks
• Leverages large-scale open-source datasets combined with in-house synthetic vision data, selected for balanced task coverage
• Follows a progressive training strategy: base model → joint mid-training → supervised fine-tuning

🏃 How to run LFM2-VL

ONNXRuntime

from transformers import AutoConfig, AutoProcessor
from transformers.image_utils import load_image
import onnxruntime
import numpy as np
from huggingface_hub import hf_hub_download

# 1. Load config, processor, and model
model_id = "onnx-community/LFM2-VL-450M-ONNX"
config = AutoConfig.from_pretrained(model_id)
processor = AutoProcessor.from_pretrained(model_id)

local_dir = 'LFM2-VL-450M-ONNX'
vision_model_path = hf_hub_download(model_id, "vision_encoder.onnx", subfolder="onnx", local_dir=local_dir)         # Download vision graph
hf_hub_download(model_id, "vision_encoder.onnx_data", subfolder="onnx", local_dir=local_dir)                        # Download vision weights
embed_model_path = hf_hub_download(model_id, "embed_tokens.onnx", subfolder="onnx", local_dir=local_dir)            # Download embed_tokens graph
hf_hub_download(model_id, "embed_tokens.onnx_data", subfolder="onnx", local_dir=local_dir)                          # Download embed_tokens weights
decoder_model_path = hf_hub_download(model_id, "decoder_model_merged.onnx", subfolder="onnx", local_dir=local_dir)  # Download decoder graph
hf_hub_download(model_id, "decoder_model_merged.onnx_data", subfolder="onnx", local_dir=local_dir)                  # Download decoder weights

## Load sessions
providers = ['CPUExecutionProvider']
vision_session = onnxruntime.InferenceSession(vision_model_path, providers=providers)
embed_session = onnxruntime.InferenceSession(embed_model_path, providers=providers)
decoder_session = onnxruntime.InferenceSession(decoder_model_path, providers=providers)

## Set config values
text_config = config.text_config
num_key_value_heads = text_config.num_key_value_heads
head_dim = text_config.hidden_size // text_config.num_attention_heads
num_hidden_layers = text_config.num_hidden_layers
eos_token_id = text_config.eos_token_id
hidden_size = text_config.hidden_size
conv_L_cache = text_config.conv_L_cache
layer_types = text_config.layer_types
image_token_index = config.image_token_index

# 2. Prepare inputs
image_url = "https://www.ilankelman.org/stopsigns/australia.jpg"
image = load_image(image_url)
messages = [
  {
    "role": "user",
    "content": [
      {"type": "image", "image": image},
      {"type": "text", "text": "What is in this image?"},
    ],
  },
]
inputs = processor.apply_chat_template(
  messages,
  add_generation_prompt=True,
  return_tensors="pt",
  return_dict=True,
  tokenize=True,
)

input_ids = inputs['input_ids'].numpy()
attention_mask = inputs['attention_mask'].numpy()
has_vision_inputs = 'pixel_values' in inputs
pixel_values = inputs['pixel_values'].numpy() if has_vision_inputs else None
pixel_attention_mask = inputs['pixel_attention_mask'].numpy().astype(np.int64) if has_vision_inputs else None
spatial_shapes = inputs['spatial_shapes'].numpy() if has_vision_inputs else None

batch_size = input_ids.shape[0]
past_cache_values = {}
for i in range(num_hidden_layers):
  if layer_types[i] == 'full_attention':
    for kv in ('key', 'value'):
      past_cache_values[f'past_key_values.{i}.{kv}'] = np.zeros([batch_size, num_key_value_heads, 0, head_dim], dtype=np.float32)
  elif layer_types[i] == 'conv':
    past_cache_values[f'past_conv.{i}'] = np.zeros([batch_size, hidden_size, conv_L_cache], dtype=np.float32)
  else:
    raise ValueError(f"Unsupported layer type: {layer_types[i]}")

# 3. Generation loop
max_new_tokens = 1024
generated_tokens = np.array([[]], dtype=np.int64)
image_features = None
for i in range(max_new_tokens):
  inputs_embeds = embed_session.run(None, {'input_ids': input_ids})[0]

  if has_vision_inputs and image_features is None:
    ## Only compute vision features if not already computed
    image_features = vision_session.run(None, dict(
      pixel_values=pixel_values,
      pixel_attention_mask=pixel_attention_mask,
      spatial_shapes=spatial_shapes,
    ))[0]

    ## Merge text and vision embeddings
    inputs_embeds[input_ids == image_token_index] = image_features.reshape(-1, image_features.shape[-1])

  logits, *present_cache_values = decoder_session.run(None, dict(
    inputs_embeds=inputs_embeds,
    attention_mask=attention_mask,
    **past_cache_values,
  ))

  ## Update values for next generation loop
  input_ids = logits[:, -1].argmax(-1, keepdims=True)
  attention_mask = np.concatenate([attention_mask, np.ones((batch_size, 1), dtype=attention_mask.dtype)], axis=-1)
  for j, key in enumerate(past_cache_values):
    past_cache_values[key] = present_cache_values[j]

  generated_tokens = np.concatenate([generated_tokens, input_ids], axis=-1)
  if np.isin(input_ids, eos_token_id).any():
    break

  ## (Optional) Streaming
  print(processor.decode(input_ids[0], skip_special_tokens=False), end='', flush=True)
print()

# 4. Output result
print(processor.batch_decode(generated_tokens, skip_special_tokens=False)[0])

🔧 How to fine-tune

We recommend fine-tuning LFM2-VL models on your use cases to maximize performance.

📈 Performance

We obtained MM-IFEval and InfoVQA (Val) scores for InternVL 3 and SmolVLM2 models using VLMEvalKit.

📬 Contact

If you are interested in custom solutions with edge deployment, please contact our sales team.

Citation

@article{liquidai2025lfm2,
 title={LFM2 Technical Report},
 author={Liquid AI},
 journal={arXiv preprint arXiv:2511.23404},
 year={2025}
}