Instructions to use Alienstro/Gemma4-E2B-it-Deepseek-V4-8000x with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Alienstro/Gemma4-E2B-it-Deepseek-V4-8000x with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Alienstro/Gemma4-E2B-it-Deepseek-V4-8000x",
	filename="gemma-4-e2b-it.BF16-mmproj.gguf",
)

llm.create_chat_completion(
	messages = "\"I like you. I love you\""
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Alienstro/Gemma4-E2B-it-Deepseek-V4-8000x with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Alienstro/Gemma4-E2B-it-Deepseek-V4-8000x:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Alienstro/Gemma4-E2B-it-Deepseek-V4-8000x:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Alienstro/Gemma4-E2B-it-Deepseek-V4-8000x:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Alienstro/Gemma4-E2B-it-Deepseek-V4-8000x: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 Alienstro/Gemma4-E2B-it-Deepseek-V4-8000x:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Alienstro/Gemma4-E2B-it-Deepseek-V4-8000x: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 Alienstro/Gemma4-E2B-it-Deepseek-V4-8000x:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Alienstro/Gemma4-E2B-it-Deepseek-V4-8000x:Q4_K_M

Use Docker

docker model run hf.co/Alienstro/Gemma4-E2B-it-Deepseek-V4-8000x:Q4_K_M

LM Studio
Jan
Ollama
How to use Alienstro/Gemma4-E2B-it-Deepseek-V4-8000x with Ollama:
```
ollama run hf.co/Alienstro/Gemma4-E2B-it-Deepseek-V4-8000x:Q4_K_M
```

Unsloth Studio new

How to use Alienstro/Gemma4-E2B-it-Deepseek-V4-8000x 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 Alienstro/Gemma4-E2B-it-Deepseek-V4-8000x 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 Alienstro/Gemma4-E2B-it-Deepseek-V4-8000x to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Alienstro/Gemma4-E2B-it-Deepseek-V4-8000x to start chatting

Pi new

How to use Alienstro/Gemma4-E2B-it-Deepseek-V4-8000x with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Alienstro/Gemma4-E2B-it-Deepseek-V4-8000x: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": "Gemma4-E2B-it-Deepseek-V4-8000x"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Alienstro/Gemma4-E2B-it-Deepseek-V4-8000x with Docker Model Runner:
```
docker model run hf.co/Alienstro/Gemma4-E2B-it-Deepseek-V4-8000x:Q4_K_M
```

Lemonade

How to use Alienstro/Gemma4-E2B-it-Deepseek-V4-8000x with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Alienstro/Gemma4-E2B-it-Deepseek-V4-8000x:Q4_K_M

Run and chat with the model

lemonade run user.Gemma4-E2B-it-Deepseek-V4-8000x-Q4_K_M

List all available models

lemonade list

Gemma4-E2B-it-Deepseek-V4-8000x : GGUF

This model was finetuned and converted to GGUF format using Unsloth.

Achieved a training loss of 1.63

Parameters

Epochs: 2
Method: QLoRA
Context length: 1024
Learning Rate: 0.0002

LoRa Settings

Rank: 16
Alpha: 16
Dropout: 0.00
Target modules: All
LoRA

Training Hyperparameters

Optimizer: Paged AdamW 8-Bit
LR scheduler: Linear
Batch Size: 1
Grad Accum: 32
Weight Decay: 0.001

Example usage:

For text only LLMs: llama-cli -hf Alienstro/Gemma4-E2B-it-Deepseek-V4-8000x --jinja
For multimodal models: llama-mtmd-cli -hf Alienstro/Gemma4-E2B-it-Deepseek-V4-8000x --jinja

Available Model files:

gemma-4-e2b-it.Q4_K_M.gguf
gemma-4-e2b-it.BF16-mmproj.gguf

⚠️ Ollama Note for Vision Models

Important: Ollama currently does not support separate mmproj files for vision models.

To create an Ollama model from this vision model:

Place the Modelfile in the same directory as the finetuned bf16 merged model
Run: ollama create model_name -f ./Modelfile (Replace model_name with your desired name)

This will create a unified bf16 model that Ollama can use. This was trained 2x faster with Unsloth

Downloads last month: 6,735

GGUF

Model size

5B params

Architecture

gemma4

Hardware compatibility

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for Alienstro/Gemma4-E2B-it-Deepseek-V4-8000x

Base model

google/gemma-4-E2B

Finetuned

google/gemma-4-E2B-it

Quantized

(169)

this model

Alienstro
/

Gemma4-E2B-it-Deepseek-V4-8000x

Gemma4-E2B-it-Deepseek-V4-8000x : GGUF

Available Model files:

⚠️ Ollama Note for Vision Models

Model tree for Alienstro/Gemma4-E2B-it-Deepseek-V4-8000x

Dataset used to train Alienstro/Gemma4-E2B-it-Deepseek-V4-8000x