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

Libraries

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

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="sowilow/LFM2.5-1.2B-Instruct-DGX-Spark-GGUF",
	filename="LFM2.5-1.2B-Instruct-Q4_K_M.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use sowilow/LFM2.5-1.2B-Instruct-DGX-Spark-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf sowilow/LFM2.5-1.2B-Instruct-DGX-Spark-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf sowilow/LFM2.5-1.2B-Instruct-DGX-Spark-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf sowilow/LFM2.5-1.2B-Instruct-DGX-Spark-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf sowilow/LFM2.5-1.2B-Instruct-DGX-Spark-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 sowilow/LFM2.5-1.2B-Instruct-DGX-Spark-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf sowilow/LFM2.5-1.2B-Instruct-DGX-Spark-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 sowilow/LFM2.5-1.2B-Instruct-DGX-Spark-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sowilow/LFM2.5-1.2B-Instruct-DGX-Spark-GGUF:Q4_K_M

Use Docker

docker model run hf.co/sowilow/LFM2.5-1.2B-Instruct-DGX-Spark-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use sowilow/LFM2.5-1.2B-Instruct-DGX-Spark-GGUF with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/sowilow/LFM2.5-1.2B-Instruct-DGX-Spark-GGUF:Q4_K_M

Ollama
How to use sowilow/LFM2.5-1.2B-Instruct-DGX-Spark-GGUF with Ollama:
```
ollama run hf.co/sowilow/LFM2.5-1.2B-Instruct-DGX-Spark-GGUF:Q4_K_M
```

Unsloth Studio

How to use sowilow/LFM2.5-1.2B-Instruct-DGX-Spark-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 sowilow/LFM2.5-1.2B-Instruct-DGX-Spark-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 sowilow/LFM2.5-1.2B-Instruct-DGX-Spark-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

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

How to use sowilow/LFM2.5-1.2B-Instruct-DGX-Spark-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf sowilow/LFM2.5-1.2B-Instruct-DGX-Spark-GGUF:Q4_K_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": "sowilow/LFM2.5-1.2B-Instruct-DGX-Spark-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use sowilow/LFM2.5-1.2B-Instruct-DGX-Spark-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf sowilow/LFM2.5-1.2B-Instruct-DGX-Spark-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 sowilow/LFM2.5-1.2B-Instruct-DGX-Spark-GGUF:Q4_K_M

Run Hermes

hermes

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

Lemonade

How to use sowilow/LFM2.5-1.2B-Instruct-DGX-Spark-GGUF with Lemonade:

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

🚀 v0.1.6: Real-time Metrics & Blackwell-Optimized Docker (Recommended)

This model is fully compatible with the DGX-Spark-llama.cpp-Bench. Experience the state-of-the-art inference engine optimized for NVIDIA Blackwell (DGX Spark) hardware.

🌟 Key Features (v0.1.6)

Real-time Performance Metrics: Now visualizes Input TPS and Output TPS during streaming.
Improved Reasoning UI: Seamlessly renders and stabilizes the model's Chain-of-Thought (CoT).
Blackwell Optimization: Native support for ARM64/SM121 and CUDA 13.0 FP4.

🐳 Quick Start

# Pull the latest optimized image
docker pull ghcr.io/sowilow/dgx-spark-llama.cpp-bench:v0.1.6

For more details, visit our GitHub Repository.

🚀 v0.1.6: 실시간 지표 및 Blackwell 최적화 도커 (권장)

이 모델은 DGX-Spark-llama.cpp-Bench 시스템에 최적화되어 있습니다. NVIDIA Blackwell (DGX Spark) 하드웨어의 성능을 최대로 활용하세요.

🌟 주요 특징 (v0.1.6)

실시간 성능 지표 시각화: 스트리밍 중 Input TPS 및 Output TPS를 실시간으로 표시합니다.
지능형 추론 UI 고도화: 모델의 생각하는 과정(CoT)을 더 안정적으로 렌더링합니다.
Blackwell 최적화: ARM64/SM121 아키텍처 및 CUDA 13.0 FP4 가속 지원.

🐳 실행 방법

# 최신 최적화 이미지 내려받기
docker pull ghcr.io/sowilow/dgx-spark-llama.cpp-bench:v0.1.6

상세한 사용법은 GitHub 리포지토리를 참조하세요.

🚀 v0.1.5: Real-time Metrics & Blackwell-Optimized Docker (Recommended)

This model is fully compatible with the DGX-Spark-llama.cpp-Bench. Experience the state-of-the-art inference engine optimized for NVIDIA Blackwell (DGX Spark) hardware.

🌟 Key Features (v0.1.5)

Real-time Performance Metrics: Now visualizes Input TPS and Output TPS during streaming.
Improved Reasoning UI: Seamlessly renders and stabilizes the model's Chain-of-Thought (CoT).
Blackwell Optimization: Native support for ARM64/SM121 and CUDA 13.0 FP4.

🐳 Quick Start

# Pull the latest optimized image
docker pull ghcr.io/sowilow/dgx-spark-llama.cpp-bench:v0.1.5

For more details, visit our GitHub Repository.

🚀 v0.1.5: 실시간 지표 및 Blackwell 최적화 도커 (권장)

이 모델은 DGX-Spark-llama.cpp-Bench 시스템에 최적화되어 있습니다. NVIDIA Blackwell (DGX Spark) 하드웨어의 성능을 최대로 활용하세요.

🌟 주요 특징 (v0.1.5)

실시간 성능 지표 시각화: 스트리밍 중 Input TPS 및 Output TPS를 실시간으로 표시합니다.
지능형 추론 UI 고도화: 모델의 생각하는 과정(CoT)을 더 안정적으로 렌더링합니다.
Blackwell 최적화: ARM64/SM121 아키텍처 및 CUDA 13.0 FP4 가속 지원.

🐳 실행 방법

# 최신 최적화 이미지 내려받기
docker pull ghcr.io/sowilow/dgx-spark-llama.cpp-bench:v0.1.5

상세한 사용법은 GitHub 리포지토리를 참조하세요.

🚀 v0.1.4: Quick Start with Blackwell-Optimized Docker (Recommended)

This model is fully compatible with the DGX-Spark-llama.cpp-Bench. Experience the best performance on NVIDIA Blackwell (DGX Spark) hardware with our optimized inference engine.

🌟 Key Features (v0.1.4)

Blackwell Optimized: Native support for ARM64/SM121 and CUDA 13.0 FP4.
Intelligent Reasoning UI: Automatic extraction and visualization of reasoning processes (CoT).
One-Click Deployment: Standardized environment via GHCR Docker image.

🐳 How to Run

# Pull the latest optimized image
docker pull ghcr.io/sowilow/dgx-spark-llama.cpp-bench:v0.1.4

# Follow the instructions in our repo to serve this model
# GitHub: https://github.com/sowilow/DGX-Spark-llama.cpp-Bench

🚀 v0.1.4: Blackwell 최적화 도커 퀵스타트 (권장)

이 모델은 DGX-Spark-llama.cpp-Bench 시스템에 최적화되어 있습니다. NVIDIA Blackwell (DGX Spark) 하드웨어의 성능을 최대로 활용하는 최적화된 추론 엔진을 경험해 보세요.

🌟 주요 특징 (v0.1.4)

Blackwell 최적화: ARM64/SM121 아키텍처 및 CUDA 13.0 FP4 하드웨어 가속 지원.
지능형 추론 UI: 모델의 생각하는 과정(CoT)을 자동으로 감지하고 시각화합니다.
간편한 배포: GHCR 도커 이미지를 통해 환경 설정 없이 즉시 실행 가능합니다.

🐳 실행 방법

# 최신 최적화 이미지 내려받기
docker pull ghcr.io/sowilow/dgx-spark-llama.cpp-bench:v0.1.4

상세한 사용법은 GitHub 리포지토리를 참조하세요.

🚀 Quick Start with Docker (Recommended)

You can easily run this model using the DGX-Spark-llama.cpp-Bench inference engine. It's pre-configured for high-performance inference on NVIDIA hardware (especially Blackwell/DGX Spark).

1. Pull the Docker Image

docker pull ghcr.io/sowilow/dgx-spark-llama.cpp-bench:latest

2. Run the Inference Server

For detailed configuration and usage, visit the GitHub Repository.

LFM2.5-1.2B-Instruct-DGX-Spark-GGUF

This repository contains GGUF-quantized weights for LFM2.5-1.2B-Instruct, specifically optimized for NVIDIA Blackwell (DGX Spark) hardware.

🚀 Key Features

Hardware Optimized: Built with CUDA 13.0 and SM121 (Blackwell) native acceleration.
Quantization:
- Q4_K_M: Balanced performance and accuracy.
- Q8_0: High precision preservation.
Base Model Integration: Linked directly to the original LiquidAI/LFM2.5-1.2B-Instruct.