Instructions to use Mediform/gemma-4-E4B-it-assistant-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Mediform/gemma-4-E4B-it-assistant-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Mediform/gemma-4-E4B-it-assistant-gguf",
	filename="gemma-4-E4B-it-assist-F16.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Mediform/gemma-4-E4B-it-assistant-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 Mediform/gemma-4-E4B-it-assistant-gguf:F16
# Run inference directly in the terminal:
llama cli -hf Mediform/gemma-4-E4B-it-assistant-gguf:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Mediform/gemma-4-E4B-it-assistant-gguf:F16
# Run inference directly in the terminal:
llama cli -hf Mediform/gemma-4-E4B-it-assistant-gguf:F16

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 Mediform/gemma-4-E4B-it-assistant-gguf:F16
# Run inference directly in the terminal:
./llama-cli -hf Mediform/gemma-4-E4B-it-assistant-gguf:F16

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 Mediform/gemma-4-E4B-it-assistant-gguf:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Mediform/gemma-4-E4B-it-assistant-gguf:F16

Use Docker

docker model run hf.co/Mediform/gemma-4-E4B-it-assistant-gguf:F16

LM Studio
Jan
Ollama
How to use Mediform/gemma-4-E4B-it-assistant-gguf with Ollama:
```
ollama run hf.co/Mediform/gemma-4-E4B-it-assistant-gguf:F16
```

Unsloth Studio

How to use Mediform/gemma-4-E4B-it-assistant-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 Mediform/gemma-4-E4B-it-assistant-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 Mediform/gemma-4-E4B-it-assistant-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Mediform/gemma-4-E4B-it-assistant-gguf to start chatting

Atomic Chat new
Docker Model Runner
How to use Mediform/gemma-4-E4B-it-assistant-gguf with Docker Model Runner:
```
docker model run hf.co/Mediform/gemma-4-E4B-it-assistant-gguf:F16
```

Lemonade

How to use Mediform/gemma-4-E4B-it-assistant-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Mediform/gemma-4-E4B-it-assistant-gguf:F16

Run and chat with the model

lemonade run user.gemma-4-E4B-it-assistant-gguf-F16

List all available models

lemonade list

gemma-4-E4B-it assistant (MTP draft) — GGUF F16

F16 GGUF conversion of google/gemma-4-E4B-it-assistant — the stock MTP (multi-token-prediction) assistant head for Gemma-4 E4B-it. Finetune revision 65892304d4eb7762acc45257a327885f7535e584.

Purpose

Draft model for speculative decoding against the stock google/gemma-4-E4B-it-qat-q4_0-gguf target (llama.cpp --spec-type draft-mtp / ngram-mod,draft-mtp, or Scribion's in-process LlamaCppGemmaEngine). The assistant reads the target's hidden states, so draft/target pairing matters:

target	draft	mean accepted len (measured)
stock QAT q4_0	this model	~2.9 / 3
stock QAT q4_0	`Mediform/gemma4-e4b-v13-assistant-rollout-gguf`	~2.1 / 3 (mismatched)

Use Mediform/gemma4-e4b-v13-assistant-rollout-gguf when the target is the v13-plainlora finetune; use this model when the target is stock QAT.

Speculative decoding is lossless — a mismatched draft only costs speed (~28 vs ~36-40 tok/s decode on M-series), never output quality.

Downloads last month: -

GGUF

Model size

78M params

Architecture

gemma4-assistant

Hardware compatibility

16-bit

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Model tree for Mediform/gemma-4-E4B-it-assistant-gguf

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