Instructions to use SandLogicTechnologies/LFM2.5-1.2B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SandLogicTechnologies/LFM2.5-1.2B-Instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="SandLogicTechnologies/LFM2.5-1.2B-Instruct-GGUF",
	filename="LFM2.5-1.2B-Instruct-IQ3_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use SandLogicTechnologies/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 SandLogicTechnologies/LFM2.5-1.2B-Instruct-GGUF:IQ3_M
# Run inference directly in the terminal:
llama cli -hf SandLogicTechnologies/LFM2.5-1.2B-Instruct-GGUF:IQ3_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf SandLogicTechnologies/LFM2.5-1.2B-Instruct-GGUF:IQ3_M
# Run inference directly in the terminal:
llama cli -hf SandLogicTechnologies/LFM2.5-1.2B-Instruct-GGUF:IQ3_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 SandLogicTechnologies/LFM2.5-1.2B-Instruct-GGUF:IQ3_M
# Run inference directly in the terminal:
./llama-cli -hf SandLogicTechnologies/LFM2.5-1.2B-Instruct-GGUF:IQ3_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 SandLogicTechnologies/LFM2.5-1.2B-Instruct-GGUF:IQ3_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf SandLogicTechnologies/LFM2.5-1.2B-Instruct-GGUF:IQ3_M

Use Docker

docker model run hf.co/SandLogicTechnologies/LFM2.5-1.2B-Instruct-GGUF:IQ3_M

LM Studio
Jan

vLLM

How to use SandLogicTechnologies/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 "SandLogicTechnologies/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": "SandLogicTechnologies/LFM2.5-1.2B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SandLogicTechnologies/LFM2.5-1.2B-Instruct-GGUF:IQ3_M

Ollama
How to use SandLogicTechnologies/LFM2.5-1.2B-Instruct-GGUF with Ollama:
```
ollama run hf.co/SandLogicTechnologies/LFM2.5-1.2B-Instruct-GGUF:IQ3_M
```

Unsloth Studio

How to use SandLogicTechnologies/LFM2.5-1.2B-Instruct-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for SandLogicTechnologies/LFM2.5-1.2B-Instruct-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for SandLogicTechnologies/LFM2.5-1.2B-Instruct-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for SandLogicTechnologies/LFM2.5-1.2B-Instruct-GGUF to start chatting

How to use SandLogicTechnologies/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 SandLogicTechnologies/LFM2.5-1.2B-Instruct-GGUF:IQ3_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "SandLogicTechnologies/LFM2.5-1.2B-Instruct-GGUF:IQ3_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use SandLogicTechnologies/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 SandLogicTechnologies/LFM2.5-1.2B-Instruct-GGUF:IQ3_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 SandLogicTechnologies/LFM2.5-1.2B-Instruct-GGUF:IQ3_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use SandLogicTechnologies/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 SandLogicTechnologies/LFM2.5-1.2B-Instruct-GGUF:IQ3_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 "SandLogicTechnologies/LFM2.5-1.2B-Instruct-GGUF:IQ3_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"

Docker Model Runner
How to use SandLogicTechnologies/LFM2.5-1.2B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/SandLogicTechnologies/LFM2.5-1.2B-Instruct-GGUF:IQ3_M
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull SandLogicTechnologies/LFM2.5-1.2B-Instruct-GGUF:IQ3_M

Run and chat with the model

lemonade run user.LFM2.5-1.2B-Instruct-GGUF-IQ3_M

List all available models

lemonade list

LFM2.5-1.2B-Instruct

LFM2.5-1.2B-Instruct is a compact instruction-tuned language model developed by Liquid AI, designed to provide efficient natural language understanding, instruction following, reasoning, and text generation while maintaining a lightweight deployment footprint. This repository contains GGUF quantized variants of the model optimized for efficient local inference using llama.cpp.

Unlike multimodal or domain-specific models, LFM2.5-1.2B-Instruct is a text-only large language model built for general-purpose language tasks. Its compact architecture enables practical deployment on consumer hardware while delivering strong conversational ability, structured generation, multilingual understanding, and instruction-following performance.

The quantized formats significantly reduce memory requirements while preserving language modeling quality, making the model well suited for local assistants, workflow automation, embedded AI applications, and resource-efficient inference.

Model Overview

Model Name: LFM2.5-1.2B-Instruct
Base Model: LiquidAI/LFM2.5-1.2B-Instruct
Architecture: Decoder-Only Transformer
Parameter Count: 2 Billion Parameters
Modalities: Text
Primary Languages: Multilingual
Developer: Liquid AI
License: Apache 2.0

Quantization Formats

This repository provides various GGUF quantized versions of the LFM2.5-1.2B-Instruct model optimized for efficient local inference using llama.cpp.

IQ3_M

Size reduction of approx 75.76% (540.54 MB) compared to 16-bit (2.18 GB)
Compact 3-bit quantization optimized for ultra-low-memory language model inference
Suitable for lightweight conversational AI, embedded deployments, and resource-constrained environments
Enables efficient execution of instruction-following and text-generation workloads on consumer hardware
Complex reasoning and long-form generation quality may be moderately reduced compared to higher-precision variants

IQ4_NL

Size reduction of approx 70.08% (667.52 MB) compared to 16-bit (2.18 GB)
Optimized 4-bit non-linear quantization balancing language quality with memory efficiency
Recommended for production conversational AI, reasoning, summarization, and structured text-generation tasks
Preserves instruction-following capability and contextual consistency across diverse NLP workloads
May require slightly increased computational resources during inference

IQ4_XS

Size reduction of approx 71.37% (637.65 MB) compared to 16-bit (2.18 GB)
Balanced 4-bit quantization delivering an effective compromise between inference efficiency and response quality
Suitable for local AI assistants, workflow automation, question answering, and multilingual text generation
Provides dependable performance across a broad range of practical language understanding tasks
Recommended for production deployments requiring efficient inference with stable output quality

Q6_K

Size reduction of approx 58.84% (918.24 MB) compared to 16-bit (2.18 GB)
Higher-precision 6-bit K-Quant format optimized for preserving reasoning capability and language generation fidelity
Better suited for analytical reasoning, coding assistance, structured generation, and complex conversational workflows
Retains more of the original model's language understanding capability compared to lower-bit variants
Recommended when response quality is prioritized over maximum memory savings

Training Background (Original Model)

LFM2.5-1.2B-Instruct is trained with an emphasis on efficient language modeling, multilingual understanding, instruction following, and conversational reasoning across diverse text corpora.

Pretraining

Large-scale language pretraining using multilingual text datasets spanning diverse domains and writing styles
Focus on contextual language understanding, knowledge acquisition, and robust text representation learning
Optimized for downstream conversational AI, reasoning, summarization, and text-generation tasks

Instruction Tuning

Further refined using instruction-following and dialogue-oriented datasets
Enhanced for conversational consistency, structured response generation, and reasoning tasks
Improved performance across question answering, summarization, workflow automation, and general assistant applications

Key Capabilities

Instruction Following Accurately follows natural language instructions across a broad range of tasks.
Conversational AI Generates coherent and context-aware responses for interactive dialogue.
Reasoning Supports logical reasoning and multi-step problem solving.
Multilingual Understanding Understands and generates text across multiple languages.
Structured Text Generation Produces well-organized outputs suitable for automation and downstream processing.
Efficient Local Deployment Quantized variants enable practical inference on consumer hardware.

Usage Example

Using llama.cpp

./llama-cli \
  -m SandLogicTechnologies/LFM2.5-1.2B-Instruct_IQ4_NL.gguf \
  -p "Summarize the following technical document and list the key takeaways."

Recommended Usecases

Conversational AI Build lightweight local AI assistants.
Question Answering Answer factual and instructional queries across diverse topics.
Content Summarization Generate concise summaries of long-form documents and articles.
Workflow Automation Produce structured text outputs for enterprise automation pipelines.
Educational Applications Support tutoring, explanations, and interactive learning experiences.
Research & Experimentation Evaluate efficient language models for local inference and edge AI deployments.

Acknowledgments

These quantized models are based on the original work by the Liquid AI development team.

Special thanks to:

The Liquid AI team for developing and releasing the LFM2.5-1.2B-Instruct model.
Georgi Gerganov and the llama.cpp open-source community for enabling efficient quantization and inference through the GGUF format.

Contact

For questions, feedback, or support, please reach out at support@sandlogic.com or visit https://www.sandlogic.com/

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GGUF

Model size

1B params

Architecture

lfm2

Hardware compatibility

3-bit

4-bit

6-bit

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

Base model

LiquidAI/LFM2.5-1.2B-Base

Finetuned

LiquidAI/LFM2.5-1.2B-Instruct

Quantized

(72)

this model