Instructions to use REDSOULTM/baxy-gemma4-E2B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use REDSOULTM/baxy-gemma4-E2B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="REDSOULTM/baxy-gemma4-E2B-GGUF",
	filename="gemma-4-E2B-it-Q4_K_M.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use REDSOULTM/baxy-gemma4-E2B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf REDSOULTM/baxy-gemma4-E2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf REDSOULTM/baxy-gemma4-E2B-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 REDSOULTM/baxy-gemma4-E2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf REDSOULTM/baxy-gemma4-E2B-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 REDSOULTM/baxy-gemma4-E2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf REDSOULTM/baxy-gemma4-E2B-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 REDSOULTM/baxy-gemma4-E2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf REDSOULTM/baxy-gemma4-E2B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/REDSOULTM/baxy-gemma4-E2B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use REDSOULTM/baxy-gemma4-E2B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "REDSOULTM/baxy-gemma4-E2B-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": "REDSOULTM/baxy-gemma4-E2B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/REDSOULTM/baxy-gemma4-E2B-GGUF:Q4_K_M

Ollama
How to use REDSOULTM/baxy-gemma4-E2B-GGUF with Ollama:
```
ollama run hf.co/REDSOULTM/baxy-gemma4-E2B-GGUF:Q4_K_M
```

Unsloth Studio

How to use REDSOULTM/baxy-gemma4-E2B-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 REDSOULTM/baxy-gemma4-E2B-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 REDSOULTM/baxy-gemma4-E2B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for REDSOULTM/baxy-gemma4-E2B-GGUF to start chatting

How to use REDSOULTM/baxy-gemma4-E2B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf REDSOULTM/baxy-gemma4-E2B-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": "REDSOULTM/baxy-gemma4-E2B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use REDSOULTM/baxy-gemma4-E2B-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 REDSOULTM/baxy-gemma4-E2B-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 REDSOULTM/baxy-gemma4-E2B-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use REDSOULTM/baxy-gemma4-E2B-GGUF with Docker Model Runner:
```
docker model run hf.co/REDSOULTM/baxy-gemma4-E2B-GGUF:Q4_K_M
```

Lemonade

How to use REDSOULTM/baxy-gemma4-E2B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull REDSOULTM/baxy-gemma4-E2B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.baxy-gemma4-E2B-GGUF-Q4_K_M

List all available models

lemonade list

Baxy — Gemma 4 E2B (GGUF, fine-tuned)

Built with Gemma. This is a fine-tune of Google's Gemma 4 E2B-it, quantized to GGUF for local inference with llama.cpp. It powers Baxy, a 100%-local Windows voice assistant.

This repo contains the GGUF artifacts that run in Baxy's production profile:

File	Size	What it is
`gemma-4-E2B-it-Q4_K_M.gguf`	~3.4 GB	The fine-tuned LLM (Q4_K_M, imatrix). Text + tool-calling.
`mmproj-F16.gguf`	~1.0 GB	Vision projector (mmproj) for Gemma 4 multimodal (screenshots).

What the fine-tune does (and what it does NOT)

The base Gemma 4 tends to invent tools that don't exist when offered a large tool catalog, and to drift to a dominant language in the short confirmation it gives after running a tool. This fine-tune targets two reliability goals, both measured live:

0% invented tools in production (with the tool array provided in the prompt).
Multilingual post-tool replies — the action confirmation comes back in the user's language (es / en / pt / fr / de / it), not always Spanish.

It is a reliability / tool-calling fine-tune, not a knowledge upgrade — Gemma is still Gemma; it just stops inventing tools and respects the input language. Routing, honesty guards, memory, RAG and computer-use live in the Baxy app, not in these weights.

Honesty note: these numbers were measured with Baxy's harness against the real LLM (physical execution mocked). They describe behavior inside Baxy's prompt + tool contract; a bare llama-cli chat won't reproduce the tool-calling gate because there are no tools in the prompt.

How to use

Download

# new unified HF CLI
hf download REDSOULTM/baxy-gemma4-E2B-GGUF gemma-4-E2B-it-Q4_K_M.gguf --local-dir ./models/E2B
hf download REDSOULTM/baxy-gemma4-E2B-GGUF mmproj-F16.gguf            --local-dir ./models/E2B

Run with llama.cpp (text)

llama-server -m gemma-4-E2B-it-Q4_K_M.gguf --host 127.0.0.1 --port 8080 -ngl 99
# then POST to http://127.0.0.1:8080/v1/chat/completions (OpenAI-compatible)

Run with vision (multimodal)

llama-mtmd-cli -m gemma-4-E2B-it-Q4_K_M.gguf --mmproj mmproj-F16.gguf --image screenshot.png -p "Describe what's on screen"

Use it inside Baxy (the full assistant)

The app downloads these files automatically. See github.com/REDSOULTM/Baxy → Installation.

Details

Base model: google/gemma-4-E2B-it (Gemma 4, ~2B active params, MatFormer).
Quantization: Q4_K_M with an importance matrix (imatrix) computed over a multilingual corpus (es/en/pt/fr/de/it). Quantized from a bf16 merge.
Chat template: Gemma 4 turn-based (<|turn>role…<turn|>); native function-calling (<|tool_call>call:NAME{…}<tool_call|>). The GGUF carries the chat template; use a recent llama.cpp build with the Gemma 4 fixes.
Hardware target: runs in ~3.4 GB; designed for a 4 GB-VRAM laptop (or CPU).

License

This model is a derivative of Gemma 4 and is distributed under the Gemma Terms of Use. By using it you agree to those terms, including Google's Prohibited Use Policy. The fine-tuning data, training scripts and the surrounding Baxy application are released under the Baxy project's own license — see the GitHub repo.

Gemma is provided under and subject to the Gemma Terms of Use found at ai.google.dev/gemma/terms.

Citation / credits

Gemma 4 — Google DeepMind.
llama.cpp — ggml-org.
Baxy — Emmanuel Villacura (REDSOULTM).

Downloads last month: 163

GGUF

Model size

5B params

Architecture

gemma4

Hardware compatibility

4-bit

Model tree for REDSOULTM/baxy-gemma4-E2B-GGUF

Base model

google/gemma-4-E2B

Finetuned

google/gemma-4-E2B-it

Quantized

(223)

this model