--- title: LFM2.5-VL-1.6B-GGUF canonical_url: "https://www.modelscope.cn/models/unsloth/LFM2.5-VL-1.6B-GGUF" md_url: "https://www.modelscope.cn/models/unsloth/LFM2.5-VL-1.6B-GGUF.md" repository: unsloth/LFM2.5-VL-1.6B-GGUF last_updated: 2026-08-05 license: other pipeline_tag: image-text-to-text tasks: - image-text-to-text base_model: - LiquidAI/LFM2.5-VL-1.6B base_model_relation: quantized library_name: - transformer - gguf - pytorch frameworks: - pytorch language: - en - ja - ko - fr - es - de - ar - zh downloads: 322 stars: 1 tags: - liquid - unsloth - lfm2 - lfm2-vl - edge - lfm2.5-vl - lfm2.5 - gguf --- # LFM2.5-VL-1.6B-GGUF > LFM2.5-VL-1.6B-GGUF - unsloth 在 ModelScope 开源的模型。[!NOTE] Includes Unsloth chat template fixes! For llama.cpp, use --jinja unsloth/LFM2.5-VL-1.6B-GGUF 是 ModelScope 魔搭社区上的image-text-to-text模型，采用 other 许可，基于 LiquidAI/LFM2.5-VL-1.6B 构建。 - **Repository**: unsloth/LFM2.5-VL-1.6B-GGUF - **License**: other - **Tasks**: image-text-to-text - **Base model**: LiquidAI/LFM2.5-VL-1.6B - **Tags**: liquid, unsloth, lfm2, lfm2-vl, edge, lfm2.5-vl, lfm2.5, gguf - **Downloads**: 322 - **Stars**: 1 - **Last updated**: 2026-08-05 Source: https://www.modelscope.cn/models/unsloth/LFM2.5-VL-1.6B-GGUF --- > [!NOTE] > Includes Unsloth **chat template fixes**!
For `llama.cpp`, use `--jinja` >

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# 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 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` 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|

| ## 🔧 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 (SFT) notebook with a LoRA adapter 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} } ```