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helenk
/
gemma-4-E4B-finetune-GGUF

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
Model card Files Files and versions
xet
Community

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
gemma-4-E4B-finetune-GGUF
5.3 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 12 commits
helenk's picture
helenk
Replace legacy hand-rolled TEMPLATE Modelfile with RENDERER gemma4 / PARSER gemma4 directives. FROM now points at gemma-4-E4B-offlineaid-Q4_K_M.gguf explicitly.
afc2ec4 verified 4 months ago
  • .gitattributes
    1.72 kB
    Phase 9 D-05: add fine-tuned Q4_K_M GGUF (LoRA merged from Phase 8) 4 months ago
  • Modelfile
    145 Bytes
    Replace legacy hand-rolled TEMPLATE Modelfile with RENDERER gemma4 / PARSER gemma4 directives. FROM now points at gemma-4-E4B-offlineaid-Q4_K_M.gguf explicitly. 4 months ago
  • README.md
    3.53 kB
    Add model card with Tier A eval results + Ollama/llama.cpp usage 4 months ago
  • config.json
    6.2 kB
    Trained with Unsloth - config 5 months ago
  • gemma-4-E4B-offlineaid-Q4_K_M.gguf
    5.3 GB
    xet
    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). 4 months ago