Instructions to use ZuzeTt/LFM2.5-VL-1.6B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ZuzeTt/LFM2.5-VL-1.6B-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="ZuzeTt/LFM2.5-VL-1.6B-GGUF")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ZuzeTt/LFM2.5-VL-1.6B-GGUF", dtype="auto")

llama-cpp-python

How to use ZuzeTt/LFM2.5-VL-1.6B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ZuzeTt/LFM2.5-VL-1.6B-GGUF",
	filename="LFM2.5-VL-1.6B-bf16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": [
				{
					"type": "text",
					"text": "Describe this image in one sentence."
				},
				{
					"type": "image_url",
					"image_url": {
						"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use ZuzeTt/LFM2.5-VL-1.6B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ZuzeTt/LFM2.5-VL-1.6B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf ZuzeTt/LFM2.5-VL-1.6B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ZuzeTt/LFM2.5-VL-1.6B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf ZuzeTt/LFM2.5-VL-1.6B-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf ZuzeTt/LFM2.5-VL-1.6B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ZuzeTt/LFM2.5-VL-1.6B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf ZuzeTt/LFM2.5-VL-1.6B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ZuzeTt/LFM2.5-VL-1.6B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/ZuzeTt/LFM2.5-VL-1.6B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use ZuzeTt/LFM2.5-VL-1.6B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ZuzeTt/LFM2.5-VL-1.6B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ZuzeTt/LFM2.5-VL-1.6B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/ZuzeTt/LFM2.5-VL-1.6B-GGUF:Q4_K_M

SGLang

How to use ZuzeTt/LFM2.5-VL-1.6B-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "ZuzeTt/LFM2.5-VL-1.6B-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ZuzeTt/LFM2.5-VL-1.6B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "ZuzeTt/LFM2.5-VL-1.6B-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ZuzeTt/LFM2.5-VL-1.6B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Ollama
How to use ZuzeTt/LFM2.5-VL-1.6B-GGUF with Ollama:
```
ollama run hf.co/ZuzeTt/LFM2.5-VL-1.6B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use ZuzeTt/LFM2.5-VL-1.6B-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ZuzeTt/LFM2.5-VL-1.6B-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ZuzeTt/LFM2.5-VL-1.6B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ZuzeTt/LFM2.5-VL-1.6B-GGUF to start chatting

Pi new

How to use ZuzeTt/LFM2.5-VL-1.6B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf ZuzeTt/LFM2.5-VL-1.6B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "ZuzeTt/LFM2.5-VL-1.6B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ZuzeTt/LFM2.5-VL-1.6B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf ZuzeTt/LFM2.5-VL-1.6B-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default ZuzeTt/LFM2.5-VL-1.6B-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use ZuzeTt/LFM2.5-VL-1.6B-GGUF with Docker Model Runner:
```
docker model run hf.co/ZuzeTt/LFM2.5-VL-1.6B-GGUF:Q4_K_M
```

Lemonade

How to use ZuzeTt/LFM2.5-VL-1.6B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ZuzeTt/LFM2.5-VL-1.6B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.LFM2.5-VL-1.6B-GGUF-Q4_K_M

List all available models

lemonade list

Using llama.cpp release b8763 for quantization. Computing over 400 chunks, n_ctx=1024

Try LFM • Docs • LEAP • Discord

LFM2.5‑VL-1.6B

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.

📄 Model details

Model	Parameters	Description
LFM2.5-1.2B-Base	1.2B	Pre-trained base model for fine-tuning
LFM2.5-1.2B-Instruct	1.2B	General-purpose instruction-tuned model
LFM2.5-1.2B-Thinking	1.2B	General-purpose reasoning model
LFM2.5-1.2B-JP	1.2B	Japanese-optimized chat model
LFM2.5-VL-1.6B	1.6B	Vision-language model with fast inference
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	Original model checkpoint in native format. Best for fine-tuning or inference with Transformers and vLLM.
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	ONNX Runtime format for cross-platform deployment. Enables hardware-accelerated inference across diverse environments (cloud, edge, mobile).
LFM2.5-VL-1.6B-MLX	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 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() to format your messages automatically.

🏃 Inference

You can run LFM2.5-VL-1.6B with Hugging Face transformers v5.1 or newer:

pip install transformers pillow

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 for the full guide.

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
Transformers	Simple inference with direct access to model internals.	Link
vLLM	High-throughput production deployments with GPU.	coming soon
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 (Unsloth)	Supervised Fine-Tuning with LoRA using Unsloth.
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. 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

Got questions or want to connect? Join our Discord community
If you are interested in custom solutions with edge deployment, please contact our sales team.

Citation

@article{liquidai2025lfm2,
 title={LFM2 Technical Report},
 author={Liquid AI},
 journal={arXiv preprint arXiv:2511.23404},
 year={2025}
}

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Paper for ZuzeTt/LFM2.5-VL-1.6B-GGUF

LFM2 Technical Report

Paper • 2511.23404 • Published Nov 28, 2025 • 61