Instructions to use kaitchup/LFM3-1.7B-GSQ-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 kaitchup/LFM3-1.7B-GSQ-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 kaitchup/LFM3-1.7B-GSQ-GGUF:IQ4_XS_RCO
# Run inference directly in the terminal:
llama cli -hf kaitchup/LFM3-1.7B-GSQ-GGUF:IQ4_XS_RCO

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf kaitchup/LFM3-1.7B-GSQ-GGUF:IQ4_XS_RCO
# Run inference directly in the terminal:
llama cli -hf kaitchup/LFM3-1.7B-GSQ-GGUF:IQ4_XS_RCO

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf kaitchup/LFM3-1.7B-GSQ-GGUF:IQ4_XS_RCO
# Run inference directly in the terminal:
./build/bin/llama cli -hf kaitchup/LFM3-1.7B-GSQ-GGUF:IQ4_XS_RCO

Use Docker

docker model run hf.co/kaitchup/LFM3-1.7B-GSQ-GGUF:IQ4_XS_RCO

Llama new
LM Studio
Jan

vLLM

How to use kaitchup/LFM3-1.7B-GSQ-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kaitchup/LFM3-1.7B-GSQ-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": "kaitchup/LFM3-1.7B-GSQ-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kaitchup/LFM3-1.7B-GSQ-GGUF:IQ4_XS_RCO

Ollama
How to use kaitchup/LFM3-1.7B-GSQ-GGUF with Ollama:
```
ollama run hf.co/kaitchup/LFM3-1.7B-GSQ-GGUF:IQ4_XS_RCO
```
Unsloth Desktop

How to use kaitchup/LFM3-1.7B-GSQ-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf kaitchup/LFM3-1.7B-GSQ-GGUF:IQ4_XS_RCO

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": "kaitchup/LFM3-1.7B-GSQ-GGUF:IQ4_XS_RCO"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use kaitchup/LFM3-1.7B-GSQ-GGUF with Docker Model Runner:
```
docker model run hf.co/kaitchup/LFM3-1.7B-GSQ-GGUF:IQ4_XS_RCO
```

Lemonade

How to use kaitchup/LFM3-1.7B-GSQ-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull kaitchup/LFM3-1.7B-GSQ-GGUF:IQ4_XS_RCO

Run and chat with the model

lemonade run user.LFM3-1.7B-GSQ-GGUF-IQ4_XS_RCO

List all available models

lemonade list

Hermes Agent

How to use kaitchup/LFM3-1.7B-GSQ-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 kaitchup/LFM3-1.7B-GSQ-GGUF:IQ4_XS_RCO

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 kaitchup/LFM3-1.7B-GSQ-GGUF:IQ4_XS_RCO

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use kaitchup/LFM3-1.7B-GSQ-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf kaitchup/LFM3-1.7B-GSQ-GGUF:IQ4_XS_RCO

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 "kaitchup/LFM3-1.7B-GSQ-GGUF:IQ4_XS_RCO" \
  --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"

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LFM3-1.7B-GSQ-GGUF

Three GGUF checkpoints of LiquidAI/LFM3-1.7B, compressed using SLQ + GSQ or RCO + GSQ.

Models

GGUF file	GGUF label	Method	File size (GB)	English EAR@10 (%) ↑
LFM3-1.7B-GSQ-Q6_K_RCO.gguf	`Q6_K_RCO`	RCO + GSQ	1.587627	98.0327
LFM3-1.7B-GSQ-Q6_K_SLQ.gguf	`Q6_K_SLQ`	SLQ + GSQ	1.587722	97.9132
LFM3-1.7B-GSQ-1GB-IQ4_XS_RCO.gguf	`IQ4_XS_RCO`	RCO + GSQ	0.999985	91.5771

All three use custom mixed precision. Filename quantization suffixes identify the main format family; the heatmaps below show the actual tensor formats. Select the exact filename when downloading.

Precision by layer

GSQ-Q6_K_RCO

GSQ-Q6_K_SLQ

GSQ-1GB-IQ4_XS_RCO

Held-out evaluation

EAR means Expected Acceptance Rate, measured against the BF16 reference. Each language uses 64 sequences of 2,048 tokens. English uses the existing FineWeb-Edu held-out set; French, Japanese, and Hindi use FineWeb2 test text. Every checkpoint in a language chart uses the same text and reference.

EAR measured on different text is not directly comparable. Compare checkpoints within each language chart.

English

French

Japanese

Hindi

Run with llama.cpp

Use a llama.cpp build with LFM3 support. For example, download the RCO + GSQ Q6_K-family checkpoint:

hf download kaitchup/LFM3-1.7B-GSQ-GGUF \
  LFM3-1.7B-GSQ-Q6_K_RCO.gguf --local-dir .

llama-server \
  --model LFM3-1.7B-GSQ-Q6_K_RCO.gguf \
  --n-gpu-layers 99 --ctx-size 8192 --port 8080

Use another exact filename from the table to select a different checkpoint.