Text Generation
GGUF
English
Chinese
Arabic
gemma4
gemma
gemma-4
quantized
q4-k-m
llama-cpp
ollama
fine-tuned
rag
offlineaid
australian-consumer-safety
anti-scam
disaster-response
conversational
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
- llama.cpp
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
Upload E4B Q4_K_M GGUF (2026-05-09 retrain) — llama.cpp convert_hf_to_gguf.py + llama-quantize Q4_K_M from peft-merged fp16 safetensors. 5.0 GB; loads in Ollama via Modelfile (FROM gemma-4-E4B-offlineaid-Q4_K_M.gguf, RENDERER gemma4). Tier A held-out: format-OK 70.3% with RAG (vs stock+RAG 53.2%); AR format 21.6%->54.1% (2.5x).
Browse files
gemma-4-E4B-offlineaid-Q4_K_M.gguf
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