zuzett/lfm2.5-vl-1.6b-gguf overview

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    "summary": "LFM2.5‑VL-1.6B is Liquid AI's refreshed version of the first vision-language model, LFM2-VL-1.6B, built on an updated backbone LFM2.5-1.2B-Base and tuned for stronger real-world performance. Find more about LFM2.5 family of models in our blog post. * **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 🎥⚡️ Alternatively, try the API model on the Playground.",
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    "readme_markdown": "---\nlibrary_name: transformers\nlicense: other\nlicense_name: lfm1.0\nlicense_link: LICENSE\nlanguage:\n- en\n- ja\n- ko\n- fr\n- es\n- de\n- ar\n- zh\npipeline_tag: image-text-to-text\ntags:\n- liquid\n- lfm2\n- lfm2-vl\n- edge\n- lfm2.5-vl\n- lfm2.5\nbase_model: LiquidAI/LFM2.5-1.2B-Base\n---\n\nUsing <a href=\"https://github.com/ggerganov/llama.cpp/\">llama.cpp</a> release <a href=\"https://github.com/ggerganov/llama.cpp/releases/tag/b8763\">b8763</a> for quantization.\nComputing over 400 chunks, n_ctx=1024\n\n<center>\n<div style=\"text-align: center;\">\n  <img \n    src=\"https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/2b08LKpev0DNEk6DlnWkY.png\" \n    alt=\"Liquid AI\"\n    style=\"width: 100%; max-width: 100%; height: auto; display: inline-block; margin-bottom: 0.5em; margin-top: 0.5em;\"\n  />\n</div>\n<div style=\"display: flex; justify-content: center; gap: 0.5em;\">\n<a href=\"https://playground.liquid.ai/chat?model=lfm2.5-vl-1.6b\"><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>\n</div>\n</center>\n\n<br>\n\n# LFM2.5‑VL-1.6B\n\nLFM2.5‑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).\n\n* **Enhanced instruction following** on vision and language tasks.\n* **Improved multilingual vision understanding** in Arabic, Chinese, French, German, Japanese, Korean, and Spanish.\n* **Robust understanding of visual content** with improved results on multi-image inputs, high-resolution images, and OCR.\n\n🎥⚡️ 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) 🎥⚡️ \n\nAlternatively, try the API model on the [Playground](https://playground.liquid.ai/chat?model=lfm2.5-vl-1.6b).\n\n## 📄 Model details\n\n| Model | Parameters | Description |\n|-------|------------|-------------|\n| [LFM2.5-1.2B-Base](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Base) | 1.2B | Pre-trained base model for fine-tuning |\n| [LFM2.5-1.2B-Instruct](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct) | 1.2B | General-purpose instruction-tuned model |\n| [LFM2.5-1.2B-Thinking](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Thinking) | 1.2B | General-purpose reasoning model |\n| [LFM2.5-1.2B-JP](https://huggingface.co/LiquidAI/LFM2.5-1.2B-JP) | 1.2B | Japanese-optimized chat model |\n| [**LFM2.5-VL-1.6B**](https://huggingface.co/LiquidAI/LFM2.5-VL-1.6B) | 1.6B | Vision-language model with fast inference |\n| [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 |\n\nLFM2.5-VL-1.6B is a general-purpose vision-language model with the following features:\n\n- **LM Backbone**: LFM2.5-1.2B-Base\n- **Vision encoder**: SigLIP2 NaFlex shape‑optimized 400M\n- **Context length**: 32,768 tokens\n- **Vocabulary size**: 65,536\n- **Languages**: English, Arabic, Chinese, French, German, Japanese, Korean, and Spanish\n- **Native resolution processing**: handles images up to 512*512 pixels without upscaling and preserves non-standard aspect ratios without distortion\n- **Tiling strategy**: splits large images into non-overlapping 512×512 patches and includes thumbnail encoding for global context\n- **Inference-time flexibility**: user-tunable maximum image tokens and tile count for speed/quality tradeoff without retraining\n- **Generation parameters**: \n  - text: `temperature=0.1`, `min_p=0.15`, `repetition_penalty=1.05`\n  - vision: `min_image_tokens=64` `max_image_tokens=256`, `do_image_splitting=True`\n\n| Model | Description |\n|-------|-------------|\n| [**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. |\n| [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. |\n| [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). |\n| [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. |\n\nWe recommend using it for general vision-language workloads, OCR or document comprehension. It’s not well-suited for knowledge-intensive tasks.\n\n### Chat Template\n\nLFM2.5-VL uses a ChatML-like format. See the [Chat Template documentation](https://docs.liquid.ai/lfm/key-concepts/chat-template#vision-models) for details.\n\n```\n<|startoftext|><|im_start|>system\nYou are a helpful multimodal assistant by Liquid AI.<|im_end|>\n<|im_start|>user\n<image>Describe this image.<|im_end|>\n<|im_start|>assistant\nThis image shows a Caenorhabditis elegans (C. elegans) nematode.<|im_end|>\n```\n\nYou can use [`processor.apply_chat_template()`](https://huggingface.co/docs/transformers/en/chat_templating_multimodal) to format your messages automatically.\n\n## 🏃 Inference\n\nYou can run LFM2.5-VL-1.6B with Hugging Face [`transformers`](https://github.com/huggingface/transformers) v5.1 or newer:\n\n```bash\npip install transformers pillow\n```\n\n```python\nfrom transformers import AutoProcessor, AutoModelForImageTextToText\nfrom transformers.image_utils import load_image\n\n# Load model and processor\nmodel_id = \"LiquidAI/LFM2.5-VL-1.6B\"\nmodel = AutoModelForImageTextToText.from_pretrained(\n    model_id,\n    device_map=\"auto\",\n    dtype=\"bfloat16\"\n)\nprocessor = AutoProcessor.from_pretrained(model_id)\n\n# Load image and create conversation\nurl = \"https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg\"\nimage = load_image(url)\nconversation = [\n    {\n        \"role\": \"user\",\n        \"content\": [\n            {\"type\": \"image\", \"image\": image},\n            {\"type\": \"text\", \"text\": \"What is in this image?\"},\n        ],\n    },\n]\n\n# Generate Answer\ninputs = processor.apply_chat_template(\n    conversation,\n    add_generation_prompt=True,\n    return_tensors=\"pt\",\n    return_dict=True,\n    tokenize=True,\n).to(model.device)\noutputs = model.generate(**inputs, max_new_tokens=64)\nprocessor.batch_decode(outputs, skip_special_tokens=True)[0]\n\n# 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.\n```\n\n### Tool Use\n\nLFM2.5 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.\n\n```python\ntools = [{\n    \"name\": \"get_weather\",\n    \"description\": \"Get current weather for a location\",\n    \"parameters\": {\n        \"type\": \"object\",\n        \"properties\": {\"location\": {\"type\": \"string\"}},\n        \"required\": [\"location\"]\n    }\n}]\n\nmessages = [{\"role\": \"user\", \"content\": \"What's the weather in Paris?\"}]\n\n# Apply chat template with tools\ninputs = processor.tokenizer.apply_chat_template(\n    messages,\n    tools=tools,\n    add_generation_prompt=True,\n    return_tensors=\"pt\",\n    return_dict=True,\n)\ninput_ids = inputs[\"input_ids\"].to(model.device)\noutputs = model.generate(input_ids, max_new_tokens=256)\nresponse = processor.tokenizer.decode(outputs[0, input_ids.shape[1]:], skip_special_tokens=False)\n\n# <|tool_call_start|>[get_weather(location=\"Paris\")]<|tool_call_end|>I am retrieving the current weather for Paris.<|im_end|>\n```\n\n| Name | Description | Docs | Notebook |\n|------|-------------|------|----------|\n| [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> |\n| [vLLM](https://github.com/vllm-project/vllm) | High-throughput production deployments with GPU. | coming soon | <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> |\n| [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> |\n\n## 🔧 Fine-tuning\n\nWe recommend fine-tuning LFM2.5-VL-1.6B model on your use cases to maximize performance.\n\n| Notebook  | Description                                                          | Link |\n|-----------|----------------------------------------------------------------------|------|\n| 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> |\n| 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> |\n\n\n## 📊 Performance\n\n| Model              | MMStar | MM-IFEval | BLINK | InfoVQA (Val) | OCRBench (v2) | RealWorldQA | MMMU (Val) | MMMB (avg) | Multilingual MMBench (avg) |\n|--------------------|--------|-----------|-------|---------------|---------------|-------------|------------|------------|----------------------------|\n| **LFM2.5-VL-1.6B** | 50.67  | 52.29     | 48.82 | 62.71         | 41.44         | 64.84       | 40.56      | 76.96      | 65.90                      |\n| LFM2-VL-1.6B       | 49.87  | 46.35     | 44.50 | 58.35         | 35.11         | 65.75       | 39.67      | 72.13      | 60.57                      |\n| InternVL3.5-1B     | 50.27  | 36.17     | 44.19 | 60.99         | 33.53         | 57.12       | 41.89      | 68.93      | 58.32                      |\n| FastVLM-1.5B       | 53.13  | 24.99     | 43.29 | 23.92         | 26.61         | 61.56       | 38.78      | 64.84      | 50.89                      |\n\nAll 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.\n\n## 📬 Contact\n\n- Got questions or want to connect? [Join our Discord community](https://discord.com/invite/liquid-ai)\n- If you are interested in custom solutions with edge deployment, please contact [our sales team](https://www.liquid.ai/contact).\n\n## Citation\n\n```\n@article{liquidai2025lfm2,\n title={LFM2 Technical Report},\n author={Liquid AI},\n journal={arXiv preprint arXiv:2511.23404},\n year={2025}\n}\n```",
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