Instructions to use NANI-Nithin/LFM2.5-VL-3B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use NANI-Nithin/LFM2.5-VL-3B-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf NANI-Nithin/LFM2.5-VL-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf NANI-Nithin/LFM2.5-VL-3B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf NANI-Nithin/LFM2.5-VL-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf NANI-Nithin/LFM2.5-VL-3B-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 NANI-Nithin/LFM2.5-VL-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf NANI-Nithin/LFM2.5-VL-3B-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 NANI-Nithin/LFM2.5-VL-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf NANI-Nithin/LFM2.5-VL-3B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/NANI-Nithin/LFM2.5-VL-3B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use NANI-Nithin/LFM2.5-VL-3B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NANI-Nithin/LFM2.5-VL-3B-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": "NANI-Nithin/LFM2.5-VL-3B-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/NANI-Nithin/LFM2.5-VL-3B-GGUF:Q4_K_M

Ollama
How to use NANI-Nithin/LFM2.5-VL-3B-GGUF with Ollama:
```
ollama run hf.co/NANI-Nithin/LFM2.5-VL-3B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use NANI-Nithin/LFM2.5-VL-3B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf NANI-Nithin/LFM2.5-VL-3B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "NANI-Nithin/LFM2.5-VL-3B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use NANI-Nithin/LFM2.5-VL-3B-GGUF with Docker Model Runner:
```
docker model run hf.co/NANI-Nithin/LFM2.5-VL-3B-GGUF:Q4_K_M
```

Lemonade

How to use NANI-Nithin/LFM2.5-VL-3B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull NANI-Nithin/LFM2.5-VL-3B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.LFM2.5-VL-3B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use NANI-Nithin/LFM2.5-VL-3B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf NANI-Nithin/LFM2.5-VL-3B-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 NANI-Nithin/LFM2.5-VL-3B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use NANI-Nithin/LFM2.5-VL-3B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf NANI-Nithin/LFM2.5-VL-3B-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "NANI-Nithin/LFM2.5-VL-3B-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

LFM2.5-VL-3B-GGUF / README.md

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metadata

license: other
license_name: lfm-1.0
license_link: https://huggingface.co/LiquidAI/LFM2.5-VL-3B
base_model: LiquidAI/LFM2.5-VL-3B
tags:
  - llama.cpp
  - gguf
  - quantized
  - vision
  - multimodal
  - lfm2
  - liquid
  - ocr
  - grounding
language:
  - en
  - ar
  - zh
  - fr
  - de
  - it
  - ja
  - ko
  - pt
  - es
  - vi
  - th
  - id
  - hi
  - ru
  - pl
pipeline_tag: image-text-to-text
library_name: llama.cpp

LFM2.5-VL-3B-GGUF

GGUF quantizations of LiquidAI/LFM2.5-VL-3B for llama.cpp.

LFM2.5-VL-3B is a 3.1B vision-language model for on-device use. It uses the LFM2.5-2.6B language backbone plus a SigLIP2 NaFlex 400M vision encoder, with 32K context. It is strong at screen/UI understanding, OCR with layout, object grounding, and tool use from text or images.

Base model: LiquidAI/LFM2.5-VL-3B
Official GGUF (Liquid): LiquidAI/LFM2.5-VL-3B-GGUF
Docs: LFM2.5-VL-3B
Architecture: LFM2.5-VL (dense)
Context: 32,768 tokens
Vocab: 128,000
License: LFM Open License v1.0

Recommended quants

Use case	Quant	Notes
Best everyday default	`Q4_K_M`	Best size/quality tradeoff
Higher quality, still small	`Q5_K_M`	Safer for OCR / grounding
Fast / low RAM	`Q3_K_M` or `IQ4_XS`	Use IQ if you have the imatrix quants
Near-lossless	`Q6_K` / `Q8_0`	Quality check / archival
Full precision	`BF16`	Source GGUF used for quantization

IQ quants (IQ2_M, IQ3_*, IQ4_*) were built with an importance matrix and are usually better than same-size standard quants at very low bits.

Available files

Standard

LFM2.5-VL-3B-BF16.gguf
LFM2.5-VL-3B-Q2_K.gguf
LFM2.5-VL-3B-Q3_K_S.gguf
LFM2.5-VL-3B-Q3_K_M.gguf
LFM2.5-VL-3B-Q3_K_L.gguf
LFM2.5-VL-3B-Q4_0.gguf
LFM2.5-VL-3B-Q4_1.gguf
LFM2.5-VL-3B-Q4_K_S.gguf
LFM2.5-VL-3B-Q4_K_M.gguf
LFM2.5-VL-3B-Q5_K_S.gguf
LFM2.5-VL-3B-Q5_K_M.gguf
LFM2.5-VL-3B-Q6_K.gguf
LFM2.5-VL-3B-Q8_0.gguf

I-quants (imatrix)

LFM2.5-VL-3B-IQ2_M.gguf
LFM2.5-VL-3B-IQ3_XXS.gguf
LFM2.5-VL-3B-IQ3_XS.gguf
LFM2.5-VL-3B-IQ3_M.gguf
LFM2.5-VL-3B-IQ4_XS.gguf
LFM2.5-VL-3B-IQ4_NL.gguf

Use a recent llama.cpp build. This is a vision model (lfm2 / LFM2.5-VL). Older binaries may fail to load it.

Quick start

# CLI
llama-cli \
  -hf NANI-Nithin/LFM2.5-VL-3B-GGUF:Q4_K_M \
  --image test_image.jpg \
  -p "What's in this image?" \
  -n 256 \
  --temp 0.2 --top-k 50 --repeat-penalty 1.0

# Local file
llama-cli \
  -m LFM2.5-VL-3B-Q4_K_M.gguf \
  --image test_image.jpg \
  -p "Read all visible text and summarize the document." \
  -n 256 \
  --temp 0.2 --top-k 50 --repeat-penalty 1.0

# Server
llama-server \
  -m LFM2.5-VL-3B-Q4_K_M.gguf \
  -c 4096 \
  --port 8080

--image-max-tokens controls the image token budget if needed.

Sampling

Liquid recommends:

temperature=0.2
top_k=50
repetition_penalty=1.0

Good for single-turn, low-latency work: OCR, screenshots, menus/signs, object localization, batch document parsing. Less suitable for long visual reasoning or highly technical blueprint analysis.

Conversion notes

Converted from the official HF checkpoint with convert_hf_to_gguf.py (bf16), then quantized with llama-quantize. IQ files used llama-imatrix + --imatrix.

This is an unofficial community quantization. For official GGUF files, use LiquidAI/LFM2.5-VL-3B-GGUF.

License and attribution

Weights follow Liquid AI's LFM Open License v1.0. Review the license on the base model page before commercial use.