Instructions to use helenk/gemma-4-E4B-finetune-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use helenk/gemma-4-E4B-finetune-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 helenk/gemma-4-E4B-finetune-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf helenk/gemma-4-E4B-finetune-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 helenk/gemma-4-E4B-finetune-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf helenk/gemma-4-E4B-finetune-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 helenk/gemma-4-E4B-finetune-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf helenk/gemma-4-E4B-finetune-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 helenk/gemma-4-E4B-finetune-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf helenk/gemma-4-E4B-finetune-GGUF:Q4_K_M

Use Docker

docker model run hf.co/helenk/gemma-4-E4B-finetune-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use helenk/gemma-4-E4B-finetune-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "helenk/gemma-4-E4B-finetune-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": "helenk/gemma-4-E4B-finetune-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/helenk/gemma-4-E4B-finetune-GGUF:Q4_K_M

Ollama
How to use helenk/gemma-4-E4B-finetune-GGUF with Ollama:
```
ollama run hf.co/helenk/gemma-4-E4B-finetune-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use helenk/gemma-4-E4B-finetune-GGUF with Docker Model Runner:
```
docker model run hf.co/helenk/gemma-4-E4B-finetune-GGUF:Q4_K_M
```

Lemonade

How to use helenk/gemma-4-E4B-finetune-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull helenk/gemma-4-E4B-finetune-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.gemma-4-E4B-finetune-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

OfflineAid — Gemma 4 E4B fine-tune (Q4_K_M GGUF)

Q4_K_M-quantized GGUF of the OfflineAid Stage-1 fine-tune. Drop-in for llama.cpp and Ollama.

Base: unsloth/gemma-4-E4B-it
LoRA: helenk/gemma-4-E4B-lora — Unsloth fine-tune on Kaggle T4
Merged fp16 source: helenk/gemma-4-E4B-finetune
Quantize chain: peft.merge_and_unload → llama.cpp/convert_hf_to_gguf.py → llama-quantize Q4_K_M
File size: ~5.0 GB

Tier A held-out eval (vs stock + RAG)

Held-out: 111 rows stratified per-language (37 EN + 37 ZH + 37 AR) from the 1,113-row helenkwok/offlineaid corpus, seed=3407. Both models served by Ollama (Q4_K_M). Greedy decoding, explicit "Answer in {language}" directive.

Language	Metric	stock + RAG	ft + RAG	Δ
EN	ROUGE-L F1	0.688	0.699	+0.011
EN	Format-OK %	91.9%	94.6%	+2.7 pp
ZH	Format-OK %	45.9%	62.2%	+16.3 pp
AR	ROUGE-L F1	0.085	0.139	+63%
AR	Format-OK %	21.6%	54.1%	+32.4 pp (2.5×)
all	ROUGE-L F1	0.334	0.355	+0.021
all	Format-OK %	53.2%	70.3%	+17.0 pp

The fine-tune's value lives in multilingual robustness, especially Arabic (format-OK 21.6% → 54.1%, ROUGE-L +63%). Reproducible via bash scripts/tier_a_pipeline_eval_only.sh in the OfflineAid repo.

Use with Ollama

ollama pull hf.co/helenk/gemma-4-E4B-finetune-GGUF

Or via a local Modelfile:

FROM /path/to/gemma-4-E4B-offlineaid-Q4_K_M.gguf
RENDERER gemma4
PARSER gemma4
PARAMETER num_ctx 32768
PARAMETER stop "<turn|>"
PARAMETER temperature 0.0

ollama create offlineaid-e4b -f Modelfile
ollama run offlineaid-e4b

Use with llama.cpp

./llama-cli \
  -m gemma-4-E4B-offlineaid-Q4_K_M.gguf \
  -p "Answer in Simplified Chinese.\n\nQUESTION: ..." \
  --temp 0.0 -n 256

Intended use

Stage 3 of the OfflineAid pipeline — Mac-side pack-builder agent loop. Pixel 7 production deployment uses stock Gemma 4 E2B + retrieval, not this fine-tune; see the project writeup for the architectural rationale.