Instructions to use bartowski/LiquidAI_LFM2.5-2.6B-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 bartowski/LiquidAI_LFM2.5-2.6B-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 bartowski/LiquidAI_LFM2.5-2.6B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf bartowski/LiquidAI_LFM2.5-2.6B-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 bartowski/LiquidAI_LFM2.5-2.6B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf bartowski/LiquidAI_LFM2.5-2.6B-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 bartowski/LiquidAI_LFM2.5-2.6B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf bartowski/LiquidAI_LFM2.5-2.6B-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 bartowski/LiquidAI_LFM2.5-2.6B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf bartowski/LiquidAI_LFM2.5-2.6B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/bartowski/LiquidAI_LFM2.5-2.6B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use bartowski/LiquidAI_LFM2.5-2.6B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bartowski/LiquidAI_LFM2.5-2.6B-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": "bartowski/LiquidAI_LFM2.5-2.6B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/bartowski/LiquidAI_LFM2.5-2.6B-GGUF:Q4_K_M

Ollama
How to use bartowski/LiquidAI_LFM2.5-2.6B-GGUF with Ollama:
```
ollama run hf.co/bartowski/LiquidAI_LFM2.5-2.6B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use bartowski/LiquidAI_LFM2.5-2.6B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf bartowski/LiquidAI_LFM2.5-2.6B-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": "bartowski/LiquidAI_LFM2.5-2.6B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use bartowski/LiquidAI_LFM2.5-2.6B-GGUF with Docker Model Runner:
```
docker model run hf.co/bartowski/LiquidAI_LFM2.5-2.6B-GGUF:Q4_K_M
```

Lemonade

How to use bartowski/LiquidAI_LFM2.5-2.6B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull bartowski/LiquidAI_LFM2.5-2.6B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.LiquidAI_LFM2.5-2.6B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use bartowski/LiquidAI_LFM2.5-2.6B-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 bartowski/LiquidAI_LFM2.5-2.6B-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 bartowski/LiquidAI_LFM2.5-2.6B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use bartowski/LiquidAI_LFM2.5-2.6B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf bartowski/LiquidAI_LFM2.5-2.6B-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 "bartowski/LiquidAI_LFM2.5-2.6B-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"

Llamacpp imatrix Quantizations of LFM2.5-2.6B by LiquidAI

Using llama.cpp release b10262 for quantization.

Original model: https://huggingface.co/LiquidAI/LFM2.5-2.6B

Model details:

Parameter count: 3B
Input support: text
MTP: no
imatrix: yes - details

How to run

Prompt format

<|startoftext|><|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
<think>

Don't know which to choose? Grab Q4_K_M (1.68GB) - usually a good mix of size and performance. Download instructions available here

Available files:

Filename	Quant type	File Size	Split	Description
LiquidAI_LFM2.5-2.6B-bf16.gguf	bf16	5.40GB	false	Full BF16 weights.
LiquidAI_LFM2.5-2.6B-Q8_0.gguf	Q8_0	2.87GB	false	Extremely high quality, generally unneeded but max available quant.
LiquidAI_LFM2.5-2.6B-Q6_K_L.gguf	Q6_K_L	2.30GB	false	Uses Q8_0 for embed and output weights. Very high quality, near perfect, recommended.
LiquidAI_LFM2.5-2.6B-Q6_K.gguf	Q6_K	2.24GB	false	Very high quality, near perfect, recommended.
LiquidAI_LFM2.5-2.6B-Q5_K_L.gguf	Q5_K_L	2.01GB	false	Uses Q8_0 for embed and output weights. High quality, recommended.
LiquidAI_LFM2.5-2.6B-Q5_K_M.gguf	Q5_K_M	1.95GB	false	High quality, recommended.
LiquidAI_LFM2.5-2.6B-Q5_K_S.gguf	Q5_K_S	1.90GB	false	High quality, recommended.
LiquidAI_LFM2.5-2.6B-Q4_1.gguf	Q4_1	1.75GB	false	Legacy format, similar performance to Q4_K_S but with improved tokens/watt on Apple silicon.
LiquidAI_LFM2.5-2.6B-Q4_K_L.gguf	Q4_K_L	1.75GB	false	Uses Q8_0 for embed and output weights. Good quality, recommended.
LiquidAI_LFM2.5-2.6B-Q4_K_M.gguf	Q4_K_M	1.68GB	false	Good quality, default size for most use cases, recommended.
LiquidAI_LFM2.5-2.6B-Q4_K_S.gguf	Q4_K_S	1.61GB	false	Slightly lower quality with more space savings, recommended.
LiquidAI_LFM2.5-2.6B-Q4_0.gguf	Q4_0	1.60GB	false	Legacy format, kept for compatibility with older tools.
LiquidAI_LFM2.5-2.6B-IQ4_NL.gguf	IQ4_NL	1.60GB	false	Similar to IQ4_XS, but slightly larger.
LiquidAI_LFM2.5-2.6B-IQ4_XS.gguf	IQ4_XS	1.52GB	false	Decent quality, smaller than Q4_K_S with similar performance, recommended.
LiquidAI_LFM2.5-2.6B-Q3_K_XL.gguf	Q3_K_XL	1.52GB	false	Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability.
LiquidAI_LFM2.5-2.6B-Q3_K_L.gguf	Q3_K_L	1.45GB	false	Lower quality but usable, good for low RAM availability.
LiquidAI_LFM2.5-2.6B-Q3_K_M.gguf	Q3_K_M	1.37GB	false	Low quality.
LiquidAI_LFM2.5-2.6B-IQ3_M.gguf	IQ3_M	1.29GB	false	Medium-low quality, new method with decent performance comparable to Q3_K_M.
LiquidAI_LFM2.5-2.6B-Q3_K_S.gguf	Q3_K_S	1.28GB	false	Low quality, not recommended.
LiquidAI_LFM2.5-2.6B-IQ3_XS.gguf	IQ3_XS	1.23GB	false	Lower quality, new method with decent performance, slightly better than Q3_K_S.
LiquidAI_LFM2.5-2.6B-Q2_K_L.gguf	Q2_K_L	1.17GB	false	Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable.
LiquidAI_LFM2.5-2.6B-IQ3_XXS.gguf	IQ3_XXS	1.13GB	false	Lower quality, new method with decent performance, comparable to Q3 quants.
LiquidAI_LFM2.5-2.6B-Q2_K.gguf	Q2_K	1.10GB	false	Very low quality but surprisingly usable.
LiquidAI_LFM2.5-2.6B-IQ2_M.gguf	IQ2_M	1.02GB	false	Relatively low quality, uses SOTA techniques to be surprisingly usable.

Download a specific file:

hf download bartowski/LiquidAI_LFM2.5-2.6B-GGUF --include "LiquidAI_LFM2.5-2.6B-Q4_K_M.gguf" --local-dir ./

Downloading using the Hugging Face CLI

Click to view download instructions

First, make sure you have the Hugging Face CLI installed:

pip install -U "huggingface_hub[cli]"

Download a specific file:

hf download bartowski/LiquidAI_LFM2.5-2.6B-GGUF --include "LiquidAI_LFM2.5-2.6B-Q4_K_M.gguf" --local-dir ./

How to run

These quants run with llama.cpp - installable in one line via llama.app:

curl -LsSf https://llama.app/install.sh | sh
llama-server -hf bartowski/LiquidAI_LFM2.5-2.6B-GGUF:Q4_K_M

llama-server includes a built-in chat web UI, served at http://localhost:8080 by default.

These quants were made with llama.cpp release b10262 - if this model's architecture is newly supported, you'll need that release or newer to run them.

They also work in: LM Studio · koboldcpp · ramalama · Jan AI · Text Generation Web UI · LoLLMs · Atomic Chat

imatrix

All quants made using imatrix option with dataset from here. The imatrix is available here: LiquidAI_LFM2.5-2.6B-imatrix.gguf.

Embed/output weights

Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to.

ARM/AVX information

llama.cpp automatically "repacks" weights into an interleaved layout at load time for faster inference on ARM and AVX machines - details in this PR. This once required downloading special Q4_0_4_4/4_8/8_8 files; those are long gone. Online repacking now covers Q4_0, IQ4_NL, and most K-quants, so no special quant choice is needed for CPU inference.

Which file should I choose?

Click here for details

An older (early 2024) but still useful write-up with charts comparing quant performances is provided by Artefact2 here

The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.

If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.

If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.

Hugging Face can also do this math for you: add your hardware in your Local Apps settings and the model page will show which files fit.

Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.

If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.

If you want to get more into the weeds, you can check out this extremely useful feature chart:

llama.cpp feature matrix

But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.

These I-quants can also be used on CPU, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.