# LFM2.5 VL 3B URL: https://interfaze.ai/models/liquidailfm25-vl-3b [All models](https://interfaze.ai/models) LFM2.5 VL 3B by LiquidAI, a image-text-to-text model with multimodal capabilities. Understand and compare multimodal features, benchmarks, and capabilities. ## Comparison | Feature | LFM2.5 VL 3B | Interfaze | | --- | --- | --- | | Input Modalities | text, image | image, text, audio, video, document | | Native OCR | No | Yes | | Long Document Processing | No | Yes | | Language Support | 16 partial | 162+ | | Native Speech-to-Text | No | Yes | | Native Object Detection | No | Yes | | Guardrail Controls | No | Yes | | Context Input Size | 32.8K | 1M | | Tool Calling | Yes | Tool calling supported + built in browser, code execution and web search | ### Scaling | Feature | LFM2.5 VL 3B | Interfaze | | --- | --- | --- | | Scaling | Self-hosted/Provider-hosted with quantization | Unlimited | [Try Interfaze](https://interfaze.ai/dashboard)[Read the Docs](https://interfaze.ai/docs) View model card on [Hugging Face](https://huggingface.co/LiquidAI/LFM2.5-VL-3B) LFM2.5-VL-3B is a multimodal variant of LFM2.5, a family of hybrid models designed for **on-device deployment**. It builds on LFM2-VL-3B with further mid- and post-training. LFM2.5-VL-3B can process both text and images, and uses the LFM2.5-2.6B language model as its backbone, combined with a SigLIP2 NaFlex vision encoder. - **Better grounding**: Improved grounding and object detection with natural language queries. - **Better OCR**: Full page OCR with layout annotation. See [layout annotation format](https://interfaze.ai/models/liquidailfm25-vl-3b#layout-annotation-format) for more information. - **Efficient inference**: 228 tok/s on an Apple M5 Max and 116 tok/s on an AMD Ryzen AI Max+ 395, in under 3.3 GB of memory. Find more information about LFM2.5-VL-3B in our [release post](https://www.liquid.ai/blog/lfm2-5-vl-3b). ![lfm2\_5\_vl\_3b\_task\_group\_averages](https://cdn-uploads.huggingface.co/production/uploads/644249b08443bce4c9890a0f/xw2m32B8IA0mRbhn_KG7f.png) > \[!NOTE\] 💻 **Demos**: Try LFM2.5-VL-3B's vision understanding capabilities in a Hugging Face space without any setup: **[Vision-capable chat in your browser](https://huggingface.co/spaces/LiquidAI/LFM2.5-VL-3B-WebGPU)**: allows you to upload images or use the webcam to capture images and let the model interact with them, as well as use tool calls and display generated bounding boxes. If you just want to chat about images, the [LiquidAI playground](http://playground.liquid.ai/chat?model=lfm2.5-vl-3b) is a fast way to do that. ## Model Details | Model | Description | | --- | --- | | LFM2.5‑VL‑3B | Original checkpoint in native format. Best for fine-tuning and inference with HF Transformers, vLLM and SGLang | | LFM2.5‑VL‑3B‑GGUF | Quantized GGUF exports of the original checkpoint. Best for CPU inference with reduced memory usage with llama.cpp | | LFM2.5‑VL‑3B‑ONNX | Quantized ONNX exports for cross-platform deployment. Enables hardware-accelerated inference across diverse environments (cloud, edge, mobile). See the demo. | | LFM2.5-VL-3B-MLX | Quantized MLX exports for Apple Silicon. Optimized for fast inference on Mac devices using the mlx-vlm framework. | - **LM Backbone**: LFM2.5-2.6B - **Vision encoder**: SigLIP2 NaFlex shape‑optimized 400M - **Vocabulary size:** 128,000 - **Context length**: 32,768 tokens - **Languages**: English, Arabic, Chinese, French, German, Italian, Japanese, Korean, Portuguese, Spanish, Vietnamese, Thai, Indonesian, Hindi, Russian, Polish - **Native resolution processing**: Uses SigLIP2's NaFlex; large images are split into non-overlapping 512×512 patches and a resized whole-image thumbnail. - **Generation parameters**: - text: `temperature=0.2`, `top_k=50`, `repetition_penalty=1.0` - vision: Use the `processor_config.json` file. We recommend using it for single-turn, high-throughput, low-latency tasks; for example, for near-realtime object detection in automotive applications, batch processing scanned documents with OCR with layout information for turning PDFs into searchable text, or for on-device translation of menus and road signs into your native language. It is not recommended for long-context, reasoning-intensive tasks, such as visual web design, or answering highly technical questions about blueprints. ### Chat Template LFM2.5 uses a ChatML-like format. See the [Chat Template documentation](https://docs.liquid.ai/lfm/key-concepts/chat-template#vision-models) for details. Example: `<|startoftext|><|im_start|>system You are a helpful assistant trained by Liquid AI.<|im_end|> <|im_start|>user What species is in this picture?<|im_end|> <|im_start|>assistant` You can use [`tokenizer.apply_chat_template()`](https://huggingface.co/docs/transformers/en/chat_templating#using-applychattemplate) to format your messages automatically. > \[!TIP\] **Note**: The `apply_chat_template()` method automatically inserts the `` tag for each image in your message. Do not include `` in your message content. ## Inference LFM2.5-VL is supported by many inference frameworks. See the [Inference documentation](https://docs.liquid.ai/lfm/inference/transformers) for the full list. | Name | Description | Docs | Notebook | | --- | --- | --- | --- | | Transformers | Simple inference with direct access to model internals. | Link | | | vLLM | High-throughput production deployments with GPU. | Link | | | SGLang | High-throughput production deployments with GPU. | Link | | | llama.cpp | Cross-platform inference with CPU offloading. | Link | | ### Quick start Quick start with Transformers (compatible with `transformers>=5.0.0`): You will need `torch`, `transformers`, and `torchvision`. ``` import torch from transformers import AutoModelForImageTextToText, AutoProcessor model_id = "LiquidAI/LFM2.5-VL-3B" model = AutoModelForImageTextToText.from_pretrained(model_id, dtype=torch.bfloat16) processor = AutoProcessor.from_pretrained(model_id) messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://placecats.com/300/200"}, {"type": "text", "text": "Describe this image."}, ], } ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) with torch.inference_mode(): output_ids = model.generate( **inputs, do_sample=True, temperature=0.2, top_k=50, repetition_penalty=1.0, max_new_tokens=256, ) generated_ids = output_ids[:, inputs["input_ids"].shape[1] :] print(processor.batch_decode(generated_ids, skip_special_tokens=True)[0]) ``` ### Tool Use LFM2.5-VL-3B supports function calling in four steps: 1. **Function definition**: Provide the list of tools as a JSON object in the system prompt, or use [`tokenizer.apply_chat_template()`](https://huggingface.co/docs/transformers/en/chat_extras#passing-tools) with `tools=...`. 2. **Function call**: By default, LFM2.5 writes Pythonic function calls (a Python list between `<|tool_call_start|>` and `<|tool_call_end|>` special tokens), as the assistant answer. 3. **Function execution**: Execute the call and return the result with the `tool` role. 4. **Final answer**: LFM2.5 interprets the tool output and returns a plain-text answer addressing the original prompt. See the [Tool Use documentation](https://docs.liquid.ai/lfm/key-concepts/tool-use) for the full guide. Example: `<|startoftext|><|im_start|>system List of tools: [{"name": "get_candidate_status", "description": "Retrieves the current status of a candidate in the recruitment process", "parameters": {"type": "object", "properties": {"candidate_id": {"type": "string", "description": "Unique identifier for the candidate"}}, "required": ["candidate_id"]}}]<|im_end|> <|im_start|>user What is the current status of candidate ID 12345?<|im_end|> <|im_start|>assistant <|tool_call_start|>[get_candidate_status(candidate_id="12345")]<|tool_call_end|>Checking the current status of candidate ID 12345.<|im_end|> <|im_start|>tool [{"candidate_id": "12345", "status": "Interview Scheduled", "position": "Clinical Research Associate", "date": "2023-11-20"}]<|im_end|> <|im_start|>assistant The candidate with ID 12345 is currently in the "Interview Scheduled" stage for the position of Clinical Research Associate, with an interview date set for 2023-11-20.<|im_end|>` ### Layout Annotation Format LFM2.5-VL-3B can do OCR with layout annotation. The layout annotation is a list of regions, each with a label, bounding box, and content. The format is: ``` image_index=