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gemma-4-E4B-it-Q5_K_M
priyankapathak/gemma-4-E4B-it-Q5_K_M
gemma-4-E4B-it-Q5_K_M is a text generation model from priyankapathak. Use it when you need the model to write or continue text. The card lists the license as other.
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5.8 GB
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.gguf5.8 GB · 100%
At a glance
- Task
- Text Generation
- License
- other
- Access
- Public
- Created
- Apr 29, 2026
- Updated
- Apr 29, 2026
- SHA
- af74e121
Try a prompt
Model card
From the Hugging Face model README
gemma-4-E4B-it — GGUF (Q5_K_M)
📊 Performance Metrics
- Hardware: AMD EPYC 7B12 (4 vCPUs)
- Size: 5.37 GB
- Speed (Generation): 4.03 tokens/sec
- Speed (Prompt): 8.83 tokens/sec
- KV Cache Usage: 0.0143 GB
- Quantization: Q5_K_M
🔷 Model Overview
This repository contains a GGUF quantized version of:
- Base Model: gemma-4-E4B-it
- Format: GGUF (optimized for llama.cpp inference)
- Precision: Q5_K_M
- Efficiency Score: 0.7509 (TPS/GB)
GGUF format provides:
- Fast loading via memory mapping
- Single-file model distribution
- Cross-platform compatibility
- Efficient inference with llama.cpp
📦 Files
| File | Description |
|---|---|
gemma-4-E4B-it-Q5_K_M.gguf | Quantized GGUF model file |
⚙️ Technical Details
| Parameter | Value |
|---|---|
| Architecture | gemma-4-E4B-it |
| Format | GGUF |
| Precision | Q5_K_M |
| Runtime | llama.cpp |
| Benchmark Hardware | AMD EPYC 7B12 (4 vCPUs) |
| Context Latency | 36.22s |
| Memory (KV) | 0.0143 GB |
⚡ Why GGUF?
GGUF is designed for efficient inference:
- Optimized for llama.cpp
- Supports CPU and GPU inference
- Single-file deployment
- Memory-mapped loading for speed
- Ideal for edge / local environments
⚠️ License & Usage
This is a converted derivative model.
- You must comply with the original model license of gemma-4-E4B-it
- This is not an official release
- No additional rights are granted
- Original ownership remains with the base model creator
🚀 Quick Start (llama.cpp)
./llama-cli -m gemma-4-E4B-it-Q5_K_M.gguf -p "Explain AI simply"
- Task
- Text Generation
- License
- other
- Created
- Apr 29, 2026
- Updated
- Apr 29, 2026