Instructions to use majentik/gemma-4-E2B-RotorQuant-GGUF-Q4_K_M 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 majentik/gemma-4-E2B-RotorQuant-GGUF-Q4_K_M 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 majentik/gemma-4-E2B-RotorQuant-GGUF-Q4_K_M:Q4_K_M # Run inference directly in the terminal: llama cli -hf majentik/gemma-4-E2B-RotorQuant-GGUF-Q4_K_M:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf majentik/gemma-4-E2B-RotorQuant-GGUF-Q4_K_M:Q4_K_M # Run inference directly in the terminal: llama cli -hf majentik/gemma-4-E2B-RotorQuant-GGUF-Q4_K_M: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 majentik/gemma-4-E2B-RotorQuant-GGUF-Q4_K_M:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf majentik/gemma-4-E2B-RotorQuant-GGUF-Q4_K_M: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 majentik/gemma-4-E2B-RotorQuant-GGUF-Q4_K_M:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf majentik/gemma-4-E2B-RotorQuant-GGUF-Q4_K_M:Q4_K_M
Use Docker
docker model run hf.co/majentik/gemma-4-E2B-RotorQuant-GGUF-Q4_K_M:Q4_K_M
- Llama new
- LM Studio
- Jan
- vLLM
How to use majentik/gemma-4-E2B-RotorQuant-GGUF-Q4_K_M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "majentik/gemma-4-E2B-RotorQuant-GGUF-Q4_K_M" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "majentik/gemma-4-E2B-RotorQuant-GGUF-Q4_K_M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/majentik/gemma-4-E2B-RotorQuant-GGUF-Q4_K_M:Q4_K_M
- Ollama
How to use majentik/gemma-4-E2B-RotorQuant-GGUF-Q4_K_M with Ollama:
ollama run hf.co/majentik/gemma-4-E2B-RotorQuant-GGUF-Q4_K_M:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use majentik/gemma-4-E2B-RotorQuant-GGUF-Q4_K_M with Docker Model Runner:
docker model run hf.co/majentik/gemma-4-E2B-RotorQuant-GGUF-Q4_K_M:Q4_K_M
- Lemonade
How to use majentik/gemma-4-E2B-RotorQuant-GGUF-Q4_K_M with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull majentik/gemma-4-E2B-RotorQuant-GGUF-Q4_K_M:Q4_K_M
Run and chat with the model
lemonade run user.gemma-4-E2B-RotorQuant-GGUF-Q4_K_M-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Commit History
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docs: card hygiene — llama-completion -no-cnv, drop fork-clone quickstart (upstream-first KV guidance)
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Card accuracy sweep: honest brand labeling, remove dead links, upstream KV tip
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docs: upstream-first KV-cache guidance (q8_0/q4_0, mainline Hadamard rotation); fork demoted to experimental
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docs: remove AWQ variants from family table (AWQ lane paused, repos private)
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docs: warn that the turboquant fork predates gemma4 support (reported in gemma-4-26B-A4B-RotorQuant#1)
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fix: family model links majentik/gemma4-* -> majentik/gemma-4-* (reported by @shramee in gemma-4-31B-TurboQuant-MLX-4bit#1)
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docs: Tier 2 polish — variant matrix + quant trade-off
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chore(card): add hardware compatibility section
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chore(card): enrich YAML frontmatter (pipeline_tag, language, library_name, inference)
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Update model card with accurate fork requirements and ecosystem status
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Add GGUF Q4_K_M quantized model
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Add model card
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initial commit
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