---
library_name: transformers
license: other
license_name: lfm1.0
license_link: LICENSE
language:
- en
- ja
- ko
- fr
- es
- de
- ar
- zh
pipeline_tag: image-text-to-text
tags:
- liquid
- lfm2
- lfm2-vl
- edge
- lfm2.5-vl
- lfm2.5
base_model: LiquidAI/LFM2.5-1.2B-Base
---
# LFM2.5‑VL-1.6B
LFM2.5‑VL-1.6B is [Liquid AI](https://www.liquid.ai/)'s refreshed version of the first vision-language model, [LFM2-VL-1.6B](https://huggingface.co/LiquidAI/LFM2-VL-1.6B), built on an updated backbone [LFM2.5-1.2B-Base](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Base) and tuned for stronger real-world performance. Find more about LFM2.5 family of models in our [blog post](https://www.liquid.ai/blog/introducing-lfm2-5-the-next-generation-of-on-device-ai).
* **Enhanced instruction following** on vision and language tasks.
* **Improved multilingual vision understanding** in Arabic, Chinese, French, German, Japanese, Korean, and Spanish.
* **Robust understanding of visual content** with improved results on multi-image inputs, high-resolution images, and OCR.
🎥⚡️ You can try LFM2.5-VL-1.6B running locally in your browser with our real-time video stream captioning [WebGPU demo](https://huggingface.co/spaces/LiquidAI/LFM2.5-VL-1.6B-WebGPU) 🎥⚡️
Alternatively, try the API model on the [Playground](https://playground.liquid.ai/chat?model=lfm2.5-vl-1.6b).
## 📄 Model details
| Model | Parameters | Description |
|-------|------------|-------------|
| [LFM2.5-1.2B-Base](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Base) | 1.2B | Pre-trained base model for fine-tuning |
| [LFM2.5-1.2B-Instruct](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct) | 1.2B | General-purpose instruction-tuned model |
| [LFM2.5-1.2B-JP](https://huggingface.co/LiquidAI/LFM2.5-1.2B-JP) | 1.2B | Japanese-optimized chat model |
| [**LFM2.5-VL-1.6B**](https://huggingface.co/LiquidAI/LFM2.5-VL-1.6B) | 1.6B | Vision-language model with fast inference |
| [LFM2.5-Audio-1.5B](https://huggingface.co/LiquidAI/LFM2.5-Audio-1.5B) | 1.5B | Audio-language model for speech and text I/O |
LFM2.5-VL-1.6B is a general-purpose vision-language model with the following features:
- **LM Backbone**: LFM2.5-1.2B-Base
- **Vision encoder**: SigLIP2 NaFlex shape‑optimized 400M
- **Context length**: 32,768 tokens
- **Vocabulary size**: 65,536
- **Languages**: English, Arabic, Chinese, French, German, Japanese, Korean, and Spanish
- **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
- **Inference-time flexibility**: user-tunable maximum image tokens and tile count for speed/quality tradeoff without retraining
- **Generation 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`
| Model | Description |
|-------|-------------|
| [**LFM2.5-VL-1.6B**](https://huggingface.co/LiquidAI/LFM2.5-VL-1.6B) | Original model checkpoint in native format. Best for fine-tuning or inference with Transformers and vLLM. |
| [LFM2.5-VL-1.6B-GGUF](https://huggingface.co/LiquidAI/LFM2.5-VL-1.6B-GGUF) | Quantized format for llama.cpp and compatible tools. Optimized for CPU inference and local deployment with reduced memory usage. |
| [LFM2.5-VL-1.6B-ONNX](https://huggingface.co/LiquidAI/LFM2.5-VL-1.6B-ONNX) | ONNX Runtime format for cross-platform deployment. Enables hardware-accelerated inference across diverse environments (cloud, edge, mobile). |
| [LFM2.5-VL-1.6B-MLX](https://huggingface.co/mlx-community/LFM2.5-VL-1.6B-8bit) | MLX format for Apple Silicon. Optimized for fast inference on Mac devices using the MLX framework. |
We recommend using it for general vision-language workloads, OCR or document comprehension. It’s not well-suited for knowledge-intensive tasks.
### Chat Template
LFM2.5-VL uses a ChatML-like format. See the [Chat Template documentation](https://docs.liquid.ai/lfm/getting-started/vision#chat-template) for details.
```
<|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|>
```
You can use [`processor.apply_chat_template()`](https://huggingface.co/docs/transformers/en/chat_templating_multimodal) to format your messages automatically.
## 🏃 Inference
You can run LFM2.5-VL-1.6B with Hugging Face [`transformers`](https://github.com/huggingface/transformers):
```bash
pip install git+https://github.com/huggingface/transformers.git@3c2517727ce28a30f5044e01663ee204deb1cdbe pillow
```
```python
from transformers import AutoProcessor, AutoModelForImageTextToText
from transformers.image_utils import load_image
# Load model and processor
model_id = "LiquidAI/LFM2.5-VL-1.6B"
model = AutoModelForImageTextToText.from_pretrained(
model_id,
device_map="auto",
dtype="bfloat16"
)
processor = AutoProcessor.from_pretrained(model_id)
# Load image and create conversation
url = "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.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 showcases the iconic Statue of Liberty standing majestically on Liberty Island in New York Harbor. The statue is positioned on a small island surrounded by calm blue waters, with the New York City skyline visible in the background.
```
### Tool Use
LFM2.5 supports function calling for text only input by applying the chat template with the tokenizer. See the [Tool Use documentation](https://docs.liquid.ai/lfm/key-concepts/tool-use) for the full guide.
```python
tools = [{
"name": "get_weather",
"description": "Get current weather for a location",
"parameters": {
"type": "object",
"properties": {"location": {"type": "string"}},
"required": ["location"]
}
}]
messages = [{"role": "user", "content": "What's the weather in Paris?"}]
# Apply chat template with tools
inputs = processor.tokenizer.apply_chat_template(
messages,
tools=tools,
add_generation_prompt=True,
return_tensors="pt",
return_dict=True,
)
input_ids = inputs["input_ids"].to(model.device)
outputs = model.generate(input_ids, max_new_tokens=256)
response = processor.tokenizer.decode(outputs[0, input_ids.shape[1]:], skip_special_tokens=False)
# <|tool_call_start|>[get_weather(location="Paris")]<|tool_call_end|>I am retrieving the current weather for Paris.<|im_end|>
```
| Name | Description | Docs | Notebook |
|------|-------------|------|----------|
| [Transformers](https://github.com/huggingface/transformers) | Simple inference with direct access to model internals. | Link| 
|
| [vLLM](https://github.com/vllm-project/vllm) | High-throughput production deployments with GPU. | coming soon | |
| [llama.cpp](https://github.com/ggml-org/llama.cpp) | Cross-platform inference with CPU offloading. | Link | |
## 🔧 Fine-tuning
We recommend fine-tuning LFM2.5-VL-1.6B model on your use cases to maximize performance.
| Notebook | Description | Link |
|-----------|----------------------------------------------------------------------|------|
| SFT (TRL) | Supervised Fine-Tuning with LoRA using TRL. | |
## 📊 Performance
| Model | MMStar | MM-IFEval | BLINK | InfoVQA (Val) | OCRBench (v2) | RealWorldQA | MMMU (Val) | MMMB (avg) | Multilingual MMBench (avg) |
|--------------------|--------|-----------|-------|---------------|---------------|-------------|------------|------------|----------------------------|
| **LFM2.5-VL-1.6B** | 50.67 | 52.29 | 48.82 | 62.71 | 41.44 | 64.84 | 40.56 | 76.96 | 65.90 |
| LFM2-VL-1.6B | 49.87 | 46.35 | 44.50 | 58.35 | 35.11 | 65.75 | 39.67 | 72.13 | 60.57 |
| InternVL3.5-1B | 50.27 | 36.17 | 44.19 | 60.99 | 33.53 | 57.12 | 41.89 | 68.93 | 58.32 |
| FastVLM-1.5B | 53.13 | 24.99 | 43.29 | 23.92 | 26.61 | 61.56 | 38.78 | 64.84 | 50.89 |
All vision benchmark scores are obtained using [VLMEvalKit](https://github.com/open-compass/VLMEvalKit). Multilingual scores are based on the average of benchmarks translated by GPT-4.1-mini from English to Arabic, Chinese, French, German, Japanese, Korean, and Spanish.
## 📬 Contact
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}
}
```a