---
title: LFM3-Omni-2B-GGUF
canonical_url: "https://www.modelscope.cn/models/LiquidAI/LFM3-Omni-2B-GGUF"
md_url: "https://www.modelscope.cn/models/LiquidAI/LFM3-Omni-2B-GGUF.md"
repository: LiquidAI/LFM3-Omni-2B-GGUF
last_updated: 2026-10-29
license: other
pipeline_tag: any-to-any
tasks:
  - any-to-any
base_model:
  - LiquidAI/LFM3-Omni-2B
base_model_relation: quantized
library_name:
  - gguf
  - pytorch
frameworks:
  - pytorch
language:
  - en
  - ar
  - zh
  - fr
  - de
  - it
  - ja
  - ko
  - pt
  - es
  - vi
  - th
  - id
  - hi
  - ru
  - pl
  - da
  - el
  - fi
  - he
  - nl
  - sv
  - tr
downloads: 189
stars: 2
tags:
  - liquid
  - lfm3
  - lfm3-omni
  - omni
  - audio
  - vision
  - gguf
  - llama.cpp
---

# LFM3-Omni-2B-GGUF

> LFM3-Omni-2B-GGUF - LiquidAI 在 ModelScope 开源的模型。GGUF version of LFM3-Omni-2B for local inference on CPUs and GPUs.

LiquidAI/LFM3-Omni-2B-GGUF 是 ModelScope 魔搭社区上的any-to-any模型，采用 other 许可，基于 LiquidAI/LFM3-Omni-2B 构建。

- **Repository**: LiquidAI/LFM3-Omni-2B-GGUF
- **License**: other
- **Tasks**: any-to-any
- **Base model**: LiquidAI/LFM3-Omni-2B
- **Tags**: liquid, lfm3, lfm3-omni, omni, audio, vision, gguf, llama.cpp
- **Downloads**: 189
- **Stars**: 2
- **Last updated**: 2026-10-29

Source: https://www.modelscope.cn/models/LiquidAI/LFM3-Omni-2B-GGUF

---

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# LFM3-Omni-2B-GGUF

GGUF version of [LFM3-Omni-2B](https://huggingface.co/LiquidAI/LFM3-Omni-2B) for local inference on CPUs and GPUs.

LFM3-Omni-2B is an omni-modal model that understands text, images, video and audio, and answers in text and speech. It is built for on-device use.

Find architecture and usage details in the [original model card](https://huggingface.co/LiquidAI/LFM3-Omni-2B).

## How to run LFM3-Omni-2B
<hr style="margin: -0.7em 0 1.2em 0; border: 0; border-top: 1px solid rgba(127, 127, 127, 0.3);" />

Recommended sampling: greedy decoding (temperature 0) for text output. For speech output, audio temperature 0.4 and audio repetition penalty 1.2.

- [Llama.app](https://llama.app) (**Use this model > Llama** in the menu), for macOS and Windows: image, audio and video input. Requires llama.cpp version [v0.8.0](https://github.com/ggml-org/llama.cpp/releases/tag/v0.8.0) or newer.
- [LM Studio](https://lmstudio.ai) (**Use this model > LM Studio** in the menu), for macOS, Windows and Linux: image input. Requires LM Studio with llama.cpp engine 2.65.1 or newer.
- [Ollama](https://ollama.com) (`ollama run lfm3-omni`), for macOS: image and audio input. Requires Ollama version [v0.40.12](https://github.com/ollama/ollama/releases/tag/v0.40.12) or newer.

## Use with llama.cpp
<hr style="margin: -0.7em 0 1.2em 0; border: 0; border-top: 1px solid rgba(127, 127, 127, 0.3);" />

> [!NOTE]
> Supported in [llama.cpp](https://github.com/ggml-org/llama.cpp) [b11960](https://github.com/ggml-org/llama.cpp/releases/tag/b11960) ([v0.8.0](https://github.com/ggml-org/llama.cpp/releases/tag/v0.8.0)) and newer, including real-time voice conversations (with tool calls) on the `/v1/realtime` WebSocket API of `llama serve`.

Add a quantization tag to pick a file. With `-hf`, llama.cpp also downloads the vision and audio projector.

LFM3-Omni-2B is a non-reasoning model: with `llama serve`, use `--reasoning off`. Start a web chat UI and an OpenAI-compatible API at `http://localhost:8080`:

```bash
llama serve -hf LiquidAI/LFM3-Omni-2B-GGUF:Q4_K_M --reasoning off --temp 0 --port 8080
```

To ask one question from the command line, use `llama cli`.

Speech recognition: use the system prompt `Perform ASR in <language>.`

```bash
llama cli -hf LiquidAI/LFM3-Omni-2B-GGUF:Q4_K_M \
  --audio speech.wav -sys "Perform ASR in English." -p "<__media__>" --temp 0
```

Image understanding:

```bash
llama cli -hf LiquidAI/LFM3-Omni-2B-GGUF:Q4_K_M \
  --image photo.png -p "Describe this image." --temp 0
```

Video understanding: the model sees the frames and hears the soundtrack of the video, use the system prompt `Answer questions about the video using what you see and hear.` Video input needs `ffmpeg` in `PATH`.

```bash
curl -sL -o reading.mp4 https://huggingface.co/datasets/raushan-testing-hf/videos-test/resolve/4cba700bd771f44d72b549253da025c32e944d42/sample_demo_1.mp4  # a child reading a book

llama cli -hf LiquidAI/LFM3-Omni-2B-GGUF:Q4_K_M \
  --video reading.mp4 -sys "Answer questions about the video using what you see and hear." \
  -p "What happens here, and what can you hear?" --temp 0
```

Speech synthesis: `--tts-lang` takes a language code (default `en`) and `--tts-voice` is `female` (default) or `male`. The audio sampling parameters of the original model card are stored in the GGUF.

```bash
llama-tts -m LFM3-Omni-2B-Q4_K_M.gguf -mm mmproj-LFM3-Omni-2B-Q8_0.gguf \
  -p "Hello world" -o out.wav
```

More options in the [llama.cpp guide](https://docs.liquid.ai/deployment/on-device/llama-cpp).