| --- |
| library_name: transformers |
| license: other |
| license_name: lfm1.0 |
| license_link: LICENSE |
| language: |
| - en |
| - ja |
| - ko |
| - fr |
| - es |
| - de |
| - ar |
| - zh |
| - pt |
| pipeline_tag: image-text-to-text |
| tags: |
| - liquid |
| - lfm2 |
| - lfm2-vl |
| - edge |
| - lfm2.5-vl |
| - lfm2.5 |
| base_model: LiquidAI/LFM2.5-350M |
| --- |
| |
| <center> |
| <div style="text-align: center;"> |
| <img |
| src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/2b08LKpev0DNEk6DlnWkY.png" |
| alt="Liquid AI" |
| style="width: 100%; max-width: 100%; height: auto; display: inline-block; margin-bottom: 0.5em; margin-top: 0.5em;" |
| /> |
| </div> |
| <div style="display: flex; justify-content: center; gap: 0.5em;"> |
| <a href="https://playground.liquid.ai/chat?model=lfm2.5-vl-450m"><strong>Try LFM</strong></a> • <a href="https://docs.liquid.ai/lfm/getting-started/welcome"><strong>Docs</strong></a> • <a href="https://leap.liquid.ai/"><strong>LEAP</strong></a> • <a href="https://discord.com/invite/liquid-ai"><strong>Discord</strong></a> |
| </div> |
| </center> |
|
|
| <br> |
|
|
| # LFM2.5‑VL-450M |
|
|
| LFM2.5‑VL-450M is [Liquid AI](https://www.liquid.ai/)'s refreshed version of the first vision-language model, [LFM2-VL-450M](https://huggingface.co/LiquidAI/LFM2-VL-450M), built on an updated backbone [LFM2.5-350M](https://huggingface.co/LiquidAI/LFM2.5-350M) and tuned for stronger real-world performance. Find more about LFM2.5 family of models in our [blog post](http://www.liquid.ai/blog/lfm2-5-vl-450m). |
|
|
| * **Enhanced instruction following** on vision and language tasks. |
| * **Improved multilingual vision understanding** in Arabic, Chinese, French, German, Japanese, Korean, Portuguese and Spanish. |
| * **Bounding box prediction and object detection** for grounded visual understanding. |
| * **Function calling support** for text-only input. |
| |
| 🎥⚡️ You can try LFM2.5-VL-450M running locally in your browser with our real-time video stream captioning [WebGPU demo](https://huggingface.co/spaces/LiquidAI/LFM2.5-VL-450M-WebGPU) 🎥⚡️ |
|
|
| Alternatively, try the API model on the [Playground](https://playground.liquid.ai/chat?model=lfm2.5-vl-450m). |
|
|
| ## 📄 Model details |
|
|
| LFM2.5-VL-450M is a general-purpose vision-language model with the following features: |
|
|
| - **LM Backbone**: LFM2.5-350M |
| - **Vision encoder**: SigLIP2 NaFlex shape‑optimized 86M |
| - **Context length**: 32,768 tokens |
| - **Vocabulary size**: 65,536 |
| - **Languages**: English, Arabic, Chinese, French, German, Japanese, Korean, Portuguese, 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=32` `max_image_tokens=256`, `do_image_splitting=True` |
| |
| | Model | Description | |
| |-------|-------------| |
| | [**LFM2.5-VL-450M**](https://huggingface.co/LiquidAI/LFM2.5-VL-450M) | Original model checkpoint in native format. Best for fine-tuning or inference with Transformers and vLLM. | |
| | [LFM2.5-VL-450M-GGUF](https://huggingface.co/LiquidAI/LFM2.5-VL-450M-GGUF) | Quantized format for llama.cpp and compatible tools. Optimized for CPU inference and local deployment with reduced memory usage. | |
| | [LFM2.5-VL-450M-ONNX](https://huggingface.co/LiquidAI/LFM2.5-VL-450M-ONNX) | ONNX Runtime format for cross-platform deployment. Enables hardware-accelerated inference across diverse environments (cloud, edge, mobile). | |
| | [LFM2.5-VL-450M-MLX-8bit](https://huggingface.co/LiquidAI/LFM2.5-VL-450M-MLX-8bit) | MLX format for Apple Silicon. Optimized for fast on-device inference on Mac with [mlx-vlm](https://github.com/Blaizzy/mlx-vlm). Also available in [4bit](https://huggingface.co/LiquidAI/LFM2.5-VL-450M-MLX-4bit), [5bit](https://huggingface.co/LiquidAI/LFM2.5-VL-450M-MLX-5bit), [6bit](https://huggingface.co/LiquidAI/LFM2.5-VL-450M-MLX-6bit), and [bf16](https://huggingface.co/LiquidAI/LFM2.5-VL-450M-MLX-bf16). | |
| |
| We recommend using it for general vision-language workloads, captioning and object detection. It’s not well-suited for knowledge-intensive tasks or fine-grained OCR. |
| |
| ### Chat Template |
| |
| LFM2.5-VL uses a ChatML-like format. See the [Chat Template documentation](https://docs.liquid.ai/lfm/key-concepts/chat-template#vision-models) for details. |
| |
| ``` |
| <|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|> |
| ``` |
| |
| 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-450M with Hugging Face [`transformers`](https://github.com/huggingface/transformers) v5.1 or newer: |
| |
| ```bash |
| pip install transformers pillow |
| ``` |
| |
| ```python |
| from transformers import AutoProcessor, AutoModelForImageTextToText |
| from transformers.image_utils import load_image |
| |
| # Load model and processor |
| model_id = "LiquidAI/LFM2.5-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://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 captures the iconic Statue of Liberty standing majestically on Liberty Island in New York City. The statue, a symbol of freedom and democracy, is prominently featured in the foreground, its greenish-gray hue contrasting beautifully with the surrounding water. |
| ``` |
| |
| ### Visual grounding |
| |
| LFM2.5-VL-450M supports bounding box prediction: |
| |
| ```python |
| url = "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" |
| image = load_image(url) |
| query = "status" |
| prompt = f'Detect all instances of: {query}. Response must be a JSON array: [{"label": ..., "bbox": [x1, y1, x2, y2]}, ...]. Coordinates are normalized to [0,1].' |
| |
| conversation = [ |
| { |
| "role": "user", |
| "content": [ |
| {"type": "image", "image": image}, |
| {"type": "text", "text": prompt}, |
| ], |
| }, |
| ] |
| |
| # 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] |
| |
| # [{"label": "statue", "bbox": [0.3, 0.25, 0.4, 0.65]}] |
| ``` |
| |
| ### 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. | <a href="https://docs.liquid.ai/lfm/inference/transformers#vision-models">Link</a>| <a href="https://colab.research.google.com/drive/1WVQpf4XrHgHFkP0FnlZfx2nK8PugvQNZ?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> | |
| | [vLLM](https://github.com/vllm-project/vllm) | High-throughput production deployments with GPU. | <a href="https://docs.liquid.ai/deployment/gpu-inference/vllm#vision-models">Link</a> | <a href="https://colab.research.google.com/drive/1sUfQlqAvuAVB4bZ6akYVQPGmHtTDUNpF?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> | |
| | [SGLang](https://github.com/vllm-project/vllm) | High-throughput production deployments with GPU. | <a href="https://docs.liquid.ai/deployment/gpu-inference/sglang#vision-models">Link</a> | <a href="https://colab.research.google.com/drive/1qJlAFag223yFOZGzuMIkYUFhybM9ao5g?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> | |
| | [llama.cpp](https://github.com/ggml-org/llama.cpp) | Cross-platform inference with CPU offloading. | <a href="https://docs.liquid.ai/lfm/inference/llama-cpp#vision-models">Link</a> | <a href="https://colab.research.google.com/drive/1q2PjE6O_AahakRlkTNJGYL32MsdUcj7b?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> | |
| |
| ## 🔧 Fine-tuning |
| |
| We recommend fine-tuning LFM2.5-VL-450M model on your use cases to maximize performance. |
| |
| | Notebook | Description | Link | |
| |-----------|----------------------------------------------------------------------|------| |
| | SFT (Unsloth) | Supervised Fine-Tuning with LoRA using Unsloth. | <a href="https://colab.research.google.com/drive/1FaR2HSe91YDe88TG97-JVxMygl-rL6vB?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> | |
| | SFT (TRL) | Supervised Fine-Tuning with LoRA using TRL. | <a href="https://colab.research.google.com/drive/10530_jt_Joa5zH2wgYlyXosypq1R7PIz?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> | |
| |
| |
| ## 📊 Performance |
| |
| LFM2.5-VL-450M improves over LFM2-VL-450M across both vision and language benchmarks, while also adding two new capabilities: bounding box prediction on RefCOCO-M and function calling support measured by BFCLv4. |
| |
| ### Vision benchmarks |
| |
| | Model | MMStar | RealWorldQA | MMBench (dev en) | MMMU (val) | POPE | MMVet | BLINK | InfoVQA (val) | OCRBench | MM-IFEval | MMMB | CountBench | RefCOCO-M | |
| |--------------------|--------|-------------|------------------|------------|------|-------|-------|---------------|----------|------------|------|------------|-----------| |
| | **LFM2.5-VL-450M** | 43.00 | 58.43 | 60.91 | 32.67 | 86.93| 41.10 | 43.92 | 43.02 | 684 | 45.00 | 68.09| 73.31 | 81.28 | |
| | LFM2-VL-450M | 40.87 | 52.03 | 56.27 | 34.44 | 83.79| 33.85 | 42.61 | 44.56 | 657 | 33.09 | 54.29| 47.64 | - | |
| | SmolVLM2-500M | 38.20 | 49.90 | 52.32 | 34.10 | 82.67| 29.90 | 40.70 | 24.64 | 609 | 11.27 | 46.79| 61.81 | - | |
| |
| 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, Portuguese, and Spanish. |
| |
| ### Language benchmarks |
| |
| | Model | GPQA | MMLU Pro | IFEval | Multi-IF | BFCLv4 | |
| |--------------------|------|----------|--------|----------|--------| |
| | **LFM2.5-VL-450M** | 25.66| 19.32 | 61.16 | 34.63 | 21.08 | |
| | LFM2-VL-450M | 23.13| 17.22 | 51.75 | 26.21 | - | |
| | SmolVLM2-500M | 23.84| 13.57 | 30.14 | 6.82 | - | |
| |
| ## 📬 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} |
| } |
| ``` |
