---
title: LFM2-VL-450M
canonical_url: "https://www.modelscope.cn/models/LiquidAI/LFM2-VL-450M"
md_url: "https://www.modelscope.cn/models/LiquidAI/LFM2-VL-450M.md"
repository: LiquidAI/LFM2-VL-450M
last_updated: 2026-07-16
license: other
pipeline_tag: image-text-to-text
tasks:
- image-text-to-text
model_type:
- lfm2_vl
architectures:
- Lfm2VlForConditionalGeneration
parameters: 450.8M
tensor_type:
- BF16
library_name:
- transformer
- safetensors
- pytorch
frameworks:
- pytorch
language:
- en
downloads: 1003
stars: 0
tags:
- liquid
- lfm2
- lfm2-vl
- edge
---
# LFM2-VL-450M
> LFM2-VL-450M - LiquidAI 在 ModelScope 开源的模型。Try LFM • Docs • LEAP • Discord
LiquidAI/LFM2-VL-450M 是 ModelScope 魔搭社区上的 450.8M 参数image-text-to-text模型，采用 other 许可。
- **Repository**: LiquidAI/LFM2-VL-450M
- **License**: other
- **Tasks**: image-text-to-text
- **Parameters**: 450.8M
- **Tags**: liquid, lfm2, lfm2-vl, edge
- **Downloads**: 1003
- **Stars**: 0
- **Last updated**: 2026-07-16
Source: https://www.modelscope.cn/models/LiquidAI/LFM2-VL-450M
---
# LFM2‑VL-450M
LFM2‑VL is [Liquid AI](https://www.liquid.ai/)'s first series of multimodal models, designed to process text and images with variable resolutions.
Built on the [LFM2](https://huggingface.co/collections/LiquidAI/lfm2-686d721927015b2ad73eaa38) backbone, it is optimized for low-latency and edge AI applications.
We're releasing the weights of two post-trained checkpoints with [450M](https://huggingface.co/LiquidAI/LFM2-VL-450M) (for highly constrained devices) and [1.6B](https://huggingface.co/LiquidAI/LFM2-VL-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](https://www.liquid.ai/blog/lfm2-vl-efficient-vision-language-models) and its language backbone in the [LFM2 blog post](https://www.liquid.ai/blog/liquid-foundation-models-v2-our-second-series-of-generative-ai-models).
## 📄 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.
| Property | [**LFM2-VL-450M**](https://huggingface.co/LiquidAI/LFM2-VL-450M) | [**LFM2-VL-1.6B**](https://huggingface.co/LiquidAI/LFM2-VL-1.6B) |
|---|---:|---:|
| **Parameters (LM only)** | 350M | 1.2B |
| **Vision encoder** | SigLIP2 NaFlex base (86M) | SigLIP2 NaFlex shape‑optimized (400M) |
| **Backbone layers** | hybrid conv+attention | hybrid conv+attention |
| **Context (text)** | 32,768 tokens | 32,768 tokens |
| **Image tokens** | dynamic, user‑tunable | dynamic, user‑tunable |
| **Vocab size** | 65,536 | 65,536 |
| **Precision** | bfloat16 | bfloat16 |
| **License** | LFM Open License v1.0 | LFM Open License v1.0 |
**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
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 (``), which is automatically replaced with the image tokens by the processor.
You can apply it using the dedicated [`.apply_chat_template()`](https://huggingface.co/docs/transformers/en/chat_templating#applychattemplate) 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
You can run LFM2-VL with Hugging Face [`transformers`](https://github.com/huggingface/transformers) v4.57 or more recent as follows:
```bash
pip install -U transformers pillow
```
Here is an example of how to generate an answer with transformers in Python:
```python
from transformers import AutoProcessor, AutoModelForImageTextToText
from transformers.image_utils import load_image
# Load model and processor
model_id = "LiquidAI/LFM2-VL-450M"
model = AutoModelForImageTextToText.from_pretrained(
model_id,
device_map="auto",
dtype="bfloat16"
)
processor = AutoProcessor.from_pretrained(model_id)
# Load image and create conversation
url = "https://www.ilankelman.org/stopsigns/australia.jpg"
image = load_image(url)
conversation = [
{
"role": "user",
"content": [
{"type": "image", "image": image},
{"type": "text", "text": "What is in this image?"},
],
},
]
# Generate Answer
inputs = processor.apply_chat_template(
conversation,
add_generation_prompt=True,
return_tensors="pt",
return_dict=True,
tokenize=True,
).to(model.device)
outputs = model.generate(**inputs, max_new_tokens=64)
processor.batch_decode(outputs, skip_special_tokens=True)[0]
# This image depicts a vibrant street scene in what appears to be a Chinatown or similar cultural area. The focal point is a large red stop sign with white lettering, mounted on a pole.
```
You can directly run and test the model with this [Colab notebook](https://colab.research.google.com/drive/11EMJhcVB6OTEuv--OePyGK86k-38WU3q?usp=sharing).
## 🔧 How to fine-tune
We recommend fine-tuning LFM2-VL models on your use cases to maximize performance.
| Notebook | Description | Link |
|-----------|----------------------------------------------------------------------|------|
| SFT (TRL) | Supervised Fine-Tuning (SFT) notebook with a LoRA adapter using TRL. | 
|
## 📈 Performance
| Model | RealWorldQA | MM-IFEval | InfoVQA (Val) | OCRBench | BLINK | MMStar | MMMU (Val) | MathVista | SEEDBench_IMG | MMVet | MME | MMLU |
|-------------------|-------------|-----------|---------------|----------|-------|--------|------------|-----------|---------------|-------|----------|-------|
| InternVL3-2B | 65.10 | 38.49 | 66.10 | 831 | 53.10 | 61.10 | 48.70 | 57.60 | 75.00 | 67.00 | 2186.40 | 64.80 |
| InternVL3-1B | 57.00 | 31.14 | 54.94 | 798 | 43.00 | 52.30 | 43.20 | 46.90 | 71.20 | 58.70 | 1912.40 | 49.80 |
| SmolVLM2-2.2B | 57.50 | 19.42 | 37.75 | 725 | 42.30 | 46.00 | 41.60 | 51.50 | 71.30 | 34.90 | 1792.50 | - |
| LFM2-VL-1.6B | 65.75 | 46.35 | 58.35 | 729 | 44.50 | 49.87 | 39.67 | 51.70 | 72.00 | 47.75 | 1756.72 | 50.99 |
| Model | RealWorldQA | MM-IFEval | InfoVQA (Val) | OCRBench | BLINK | MMStar | MMMU (Val) | MathVista | SEEDBench_IMG | MMVet | MME | MMLU |
|-------------------|-------------|-----------|---------------|----------|-------|--------|------------|-----------|---------------|-------|----------|-------|
| SmolVLM2-500M | 49.90 | 11.27 | 24.64 | 609 | 40.70 | 38.20 | 34.10 | 37.50 | 62.20 | 29.90 | 1448.30 | - |
| LFM2-VL-450M | 52.03 | 33.09 | 44.56 | 657 | 42.61 | 40.87 | 34.44 | 45.30 | 63.62 | 33.85 | 1229.91 | 40.16 |
We obtained MM-IFEval and InfoVQA (Val) scores for InternVL 3 and SmolVLM2 models using VLMEvalKit.
## 📬 Contact
- Got questions or want to connect? [Join our Discord community](https://discord.com/invite/liquid-ai)
- If you are interested in custom solutions with edge deployment, please contact [our sales team](https://www.liquid.ai/contact).
## Citation
```
@article{liquidai2025lfm2,
title={LFM2 Technical Report},
author={Liquid AI},
journal={arXiv preprint arXiv:2511.23404},
year={2025}
}
```
