Instructions to use lyimo/gemma3n-E2B-swahili-reasoning-lora-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lyimo/gemma3n-E2B-swahili-reasoning-lora-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="lyimo/gemma3n-E2B-swahili-reasoning-lora-GGUF",
	filename="gemma3n-E2B-swahili-reasoning-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use lyimo/gemma3n-E2B-swahili-reasoning-lora-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 lyimo/gemma3n-E2B-swahili-reasoning-lora-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf lyimo/gemma3n-E2B-swahili-reasoning-lora-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf lyimo/gemma3n-E2B-swahili-reasoning-lora-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf lyimo/gemma3n-E2B-swahili-reasoning-lora-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 lyimo/gemma3n-E2B-swahili-reasoning-lora-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf lyimo/gemma3n-E2B-swahili-reasoning-lora-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 lyimo/gemma3n-E2B-swahili-reasoning-lora-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf lyimo/gemma3n-E2B-swahili-reasoning-lora-GGUF:Q4_K_M

Use Docker

docker model run hf.co/lyimo/gemma3n-E2B-swahili-reasoning-lora-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use lyimo/gemma3n-E2B-swahili-reasoning-lora-GGUF with Ollama:
```
ollama run hf.co/lyimo/gemma3n-E2B-swahili-reasoning-lora-GGUF:Q4_K_M
```

Unsloth Studio

How to use lyimo/gemma3n-E2B-swahili-reasoning-lora-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 lyimo/gemma3n-E2B-swahili-reasoning-lora-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 lyimo/gemma3n-E2B-swahili-reasoning-lora-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for lyimo/gemma3n-E2B-swahili-reasoning-lora-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use lyimo/gemma3n-E2B-swahili-reasoning-lora-GGUF with Docker Model Runner:
```
docker model run hf.co/lyimo/gemma3n-E2B-swahili-reasoning-lora-GGUF:Q4_K_M
```

Lemonade

How to use lyimo/gemma3n-E2B-swahili-reasoning-lora-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull lyimo/gemma3n-E2B-swahili-reasoning-lora-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.gemma3n-E2B-swahili-reasoning-lora-GGUF-Q4_K_M

List all available models

lemonade list

Gemma 3n E2B Swahili Reasoning LoRA - GGUF

GGUF quantized versions of lyimo/gemma3n-E2B-swahili-reasoning-lora for efficient inference on mobile devices and consumer hardware.

Available Quantizations

File	Quant	Size	Description
`gemma3n-E2B-swahili-reasoning-Q4_K_M.gguf`	Q4_K_M	2.6 GB	Recommended for mobile - Best balance of quality and size
`gemma3n-E2B-swahili-reasoning-Q8_0.gguf`	Q8_0	4.5 GB	Higher quality, larger size

Usage

With llama.cpp

./llama-cli -m gemma3n-E2B-swahili-reasoning-Q4_K_M.gguf -p "Your prompt here"

With Ollama

# Create a Modelfile
echo 'FROM ./gemma3n-E2B-swahili-reasoning-Q4_K_M.gguf' > Modelfile
ollama create gemma3n-swahili -f Modelfile
ollama run gemma3n-swahili

Model Details

Architecture: Gemma3n (6B parameters, E2B variant)
Base Model: unsloth/gemma-3n-e2b-it-unsloth-bnb-4bit
Fine-tuned for: Swahili reasoning tasks
Context Length: 32,768 tokens
Vocab Size: 262,400

Quantization Details

Converted from the original safetensors weights using llama.cpp
Q4_K_M uses mixed 4-bit quantization (4.91 BPW) - ideal for mobile deployment
Q8_0 uses 8-bit quantization (8.50 BPW) - higher quality for desktop use

Downloads last month: 5

GGUF

Model size

4B params

Architecture

gemma3n

Hardware compatibility

4-bit

8-bit

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Model tree for lyimo/gemma3n-E2B-swahili-reasoning-lora-GGUF

Base model

lyimo/gemma3n-E2B-swahili-reasoning-lora

Quantized

(1)

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