How to use from
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 NobodyWho/Google_Gemma4-E4B-GGUF:
# Run inference directly in the terminal:
llama cli -hf NobodyWho/Google_Gemma4-E4B-GGUF:
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf NobodyWho/Google_Gemma4-E4B-GGUF:
# Run inference directly in the terminal:
llama cli -hf NobodyWho/Google_Gemma4-E4B-GGUF:
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 NobodyWho/Google_Gemma4-E4B-GGUF:
# Run inference directly in the terminal:
./llama-cli -hf NobodyWho/Google_Gemma4-E4B-GGUF:
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 NobodyWho/Google_Gemma4-E4B-GGUF:
# Run inference directly in the terminal:
./build/bin/llama-cli -hf NobodyWho/Google_Gemma4-E4B-GGUF:
Use Docker
docker model run hf.co/NobodyWho/Google_Gemma4-E4B-GGUF:
Quick Links

NobodyWho/Google_Gemma4-E4B-GGUF

Overview

GGUF quantization of Google's Gemma 4 E4B instruction-tuned model, re-hosted for NobodyWho. The unsloth build already ships a tool-calling setup and recommended sampling metadata (general.sampling: temp 1.0, top_k 64, top_p 0.95), so nothing needs patching — the model is verified with NobodyWho's test suite. Gemma 4 is multimodal (text + image) with broad multilingual coverage and is released under the Apache 2.0 license.

Model Capabilities

  • Text generation — instruction-following chat
  • Tool calling — native function calling with grammar-constrained output
  • Vision — image understanding via the companion mmproj-BF16.gguf projection model
  • Long context — 128k tokens
  • Multilingual — 140+ languages

Available Quantizations

File Approach Tool-calling tests
gemma-4-E4B-it-BF16.gguf Sampling embedded upstream 14/14
gemma-4-E4B-it-Q8_0.gguf Sampling embedded upstream 14/14
gemma-4-E4B-it-Q4_K_M.gguf Sampling embedded upstream 14/14
mmproj-BF16.gguf Vision projection (use with any of the above)

Verified with NobodyWho's tool-calling suite across BF16 / Q8_0 / Q4_K_M (14/14 each, June 2026); vision and multilingual verified. Quant names follow the unsloth gemma-4-E4B-it-GGUF repo.

Quick Start

Using the NobodyWho library:

from nobodywho import Chat

chat = Chat("huggingface:NobodyWho/Google_Gemma4-E4B-GGUF/gemma-4-E4B-it-Q4_K_M.gguf")
response = chat.ask("What is the capital of Denmark?").completed()
print(response)  # The capital of Denmark is Copenhagen.

Vision

from nobodywho import Model, Chat, Prompt, Image, Text

model = Model(
    "huggingface:NobodyWho/Google_Gemma4-E4B-GGUF/gemma-4-E4B-it-Q4_K_M.gguf",
    projection_model_path="huggingface:NobodyWho/Google_Gemma4-E4B-GGUF/mmproj-BF16.gguf",
)
chat = Chat(model=model, system_prompt="You are a helpful assistant.")
response = chat.ask(Prompt([
    Text("What is in this image?"),
    Image("./photo.png"),
])).completed()
print(response)

llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
    repo_id="NobodyWho/Google_Gemma4-E4B-GGUF",
    filename="gemma-4-E4B-it-Q4_K_M.gguf",
)

Model Specifications

  • Parameters: ≈4.5B effective (E4B)
  • Context length: 131,072 tokens
  • License: Apache 2.0
  • Base model: google/gemma-4-E4B-it
  • Architecture: gemma4 (vision-capable)

Licensing / Credits

Licensed under Apache 2.0 (unchanged from upstream). All model credit belongs to Google DeepMind. GGUF quantizations provided by unsloth.

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