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
File size: 1,133 Bytes
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tags:
- gguf
- llama.cpp
- unsloth
- vision-language-model
---
# gemma_4_finetune : GGUF
This model was finetuned and converted to GGUF format using [Unsloth](https://github.com/unslothai/unsloth).
**Example usage**:
- For text only LLMs: `llama-cli -hf helenk/gemma_4_finetune --jinja`
- For multimodal models: `llama-mtmd-cli -hf helenk/gemma_4_finetune --jinja`
## Available Model files:
- `gemma-4-e4b-it.Q4_K_M.gguf`
- `gemma-4-e4b-it.F16-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:
1. Place the `Modelfile` in the same directory as the finetuned bf16 merged model
3. 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](https://github.com/unslothai/unsloth)
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
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