Instructions to use jc-builds/LFM2.5-1.2B-Instruct-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 jc-builds/LFM2.5-1.2B-Instruct-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 jc-builds/LFM2.5-1.2B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf jc-builds/LFM2.5-1.2B-Instruct-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 jc-builds/LFM2.5-1.2B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf jc-builds/LFM2.5-1.2B-Instruct-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 jc-builds/LFM2.5-1.2B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf jc-builds/LFM2.5-1.2B-Instruct-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 jc-builds/LFM2.5-1.2B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf jc-builds/LFM2.5-1.2B-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/jc-builds/LFM2.5-1.2B-Instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use jc-builds/LFM2.5-1.2B-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jc-builds/LFM2.5-1.2B-Instruct-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": "jc-builds/LFM2.5-1.2B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jc-builds/LFM2.5-1.2B-Instruct-GGUF:Q4_K_M

Ollama
How to use jc-builds/LFM2.5-1.2B-Instruct-GGUF with Ollama:
```
ollama run hf.co/jc-builds/LFM2.5-1.2B-Instruct-GGUF:Q4_K_M
```
Unsloth Desktop

How to use jc-builds/LFM2.5-1.2B-Instruct-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jc-builds/LFM2.5-1.2B-Instruct-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": "jc-builds/LFM2.5-1.2B-Instruct-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use jc-builds/LFM2.5-1.2B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/jc-builds/LFM2.5-1.2B-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use jc-builds/LFM2.5-1.2B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull jc-builds/LFM2.5-1.2B-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.LFM2.5-1.2B-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use jc-builds/LFM2.5-1.2B-Instruct-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 jc-builds/LFM2.5-1.2B-Instruct-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 jc-builds/LFM2.5-1.2B-Instruct-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use jc-builds/LFM2.5-1.2B-Instruct-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jc-builds/LFM2.5-1.2B-Instruct-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 "jc-builds/LFM2.5-1.2B-Instruct-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"

LFM2.5-1.2B-Instruct — GGUF (iPhone-optimized)

The official Q4_K_M GGUF of LiquidAI/LFM2.5-1.2B-Instruct, mirrored for on-device inference on iPhone, iPad, and Apple Silicon Mac via llama.cpp or apps that wrap it (e.g. Haplo).

Hosted by jc-builds for the Haplo ecosystem. The file is byte-identical to Liquid AI's official LFM2.5-1.2B-Instruct-Q4_K_M.gguf. Original weights © Liquid AI, redistributed under the LFM Open License v1.0 (see LICENSE).

TL;DR

A 1.2B instruct model from Liquid AI built on a hybrid architecture (short-convolution blocks mixed with a few grouped-query attention layers), which keeps the KV cache small and decoding fast on phones. Strong instruction following for its size. See the upstream model card for benchmarks.

Available quantizations

File	Size	Recommended use
`LFM2.5-1.2B-Instruct-Q4_K_M.gguf`	0.73 GB	Default — works on every device

Details


Parameters	1.2B
Architecture	`lfm2` (10 short-conv + 6 GQA layers)
Quantization	Q4_K_M
Chat format	ChatML
Minimum device	Any iPhone that runs Haplo

How to use

Download URL:

https://huggingface.co/jc-builds/LFM2.5-1.2B-Instruct-GGUF/resolve/main/LFM2.5-1.2B-Instruct-Q4_K_M.gguf

llama.cpp

llama-cli -hf jc-builds/LFM2.5-1.2B-Instruct-GGUF:Q4_K_M

License

LFM Open License v1.0 (see LICENSE). It is based on Apache 2.0 with one addition: commercial use is licensed only while the licensee's annual revenue (including affiliates) is below US$10,000,000. Review the full terms before commercial use.

Downloads last month: 53

GGUF

Model size

1B params

Architecture

lfm2

Hardware compatibility

4-bit

Model tree for jc-builds/LFM2.5-1.2B-Instruct-GGUF

Base model

LiquidAI/LFM2.5-1.2B-Base

Finetuned

LiquidAI/LFM2.5-1.2B-Instruct

Quantized

(101)

this model