Modelpublic

sowilow/gemma-4-e2b-it-DGX-Spark-GGUF

sourceHugging Facegemmaupdated 6mo agoView on Hugging Face

0likes140downloads

Model Card

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

This model is fully compatible with the [DGX-Spark-llama.cpp-Bench](https://github.com/sowilow/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

bash

# 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](https://github.com/sowilow/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

bash

# 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)

🌟 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

bash

# 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)

🌟 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

bash

# 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](https://github.com/sowilow/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

bash

# 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](https://github.com/sowilow/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

bash

# 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

bash

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.

gemma-4-e2b-it-GGUF

This repository contains GGUF-quantized weights for Gemma-4-E2B-it, specifically optimized for NVIDIA Blackwell (DGX Spark) hardware.

🚀 Key Features

—Hardware Optimized: Built with CUDA 13.0 and SM121 (Blackwell) native acceleration.
—Quantization: Q4KM (4-bit unified quantization) for ultra-low latency vision tasks.
—Base Model Integration: Linked directly to the original google/gemma-4-E2B-it.

⚖️ License & Attribution

This model is a quantized version of the original google/gemma-4-E2B-it and is subject to the Gemma License Agreement.

📂 Files Included

—gemma-4-e2b-it-q4_k_m.gguf: Main model weights.
—gemma-4-e2b-vision-mmproj-f16.gguf: Multimodal vision projector (Dimension-matched: n_embd=1536).

Created using [DGX-Spark-llama.cpp-Bench](https://github.com/sowilow/DGX-Spark-llama.cpp-Bench)