Instructions to use finis-est/gemma-4-31b-larkspur-v0.5-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 finis-est/gemma-4-31b-larkspur-v0.5-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 finis-est/gemma-4-31b-larkspur-v0.5-Q4_K_M:Q4_K_M # Run inference directly in the terminal: llama cli -hf finis-est/gemma-4-31b-larkspur-v0.5-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 finis-est/gemma-4-31b-larkspur-v0.5-Q4_K_M:Q4_K_M # Run inference directly in the terminal: llama cli -hf finis-est/gemma-4-31b-larkspur-v0.5-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 finis-est/gemma-4-31b-larkspur-v0.5-Q4_K_M:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf finis-est/gemma-4-31b-larkspur-v0.5-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 finis-est/gemma-4-31b-larkspur-v0.5-Q4_K_M:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf finis-est/gemma-4-31b-larkspur-v0.5-Q4_K_M:Q4_K_M
Use Docker
docker model run hf.co/finis-est/gemma-4-31b-larkspur-v0.5-Q4_K_M:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use finis-est/gemma-4-31b-larkspur-v0.5-Q4_K_M with Ollama:
ollama run hf.co/finis-est/gemma-4-31b-larkspur-v0.5-Q4_K_M:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use finis-est/gemma-4-31b-larkspur-v0.5-Q4_K_M with Docker Model Runner:
docker model run hf.co/finis-est/gemma-4-31b-larkspur-v0.5-Q4_K_M:Q4_K_M
- Lemonade
How to use finis-est/gemma-4-31b-larkspur-v0.5-Q4_K_M with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull finis-est/gemma-4-31b-larkspur-v0.5-Q4_K_M:Q4_K_M
Run and chat with the model
lemonade run user.gemma-4-31b-larkspur-v0.5-Q4_K_M-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Gemma 4 31B Larkspur v0.5 โ Q4_K_M GGUF
Q4_K_M quantization of trashpanda-org/gemma-4-31b-larkspur-v0.5.
Quant Details
| Property | Value |
|---|---|
| Source | trashpanda-org/gemma-4-31b-larkspur-v0.5 |
| Quant | Q4_K_M (4.83 BPW) |
| Size | ~18 GB |
| Format | GGUF (llama.cpp) |
| Original Precision | bf16 |
Usage
Load with any llama.cpp-compatible runtime (llama.cpp, KoboldCpp, ollama, LM Studio, etc.):
llama-cli -m gemma-4-31b-larkspur-v0.5-Q4_K_M.gguf -p "Your prompt here"
Notes
- Quantized from the bf16 source weights using llama.cpp's
convert_hf_to_gguf.pyโllama-quantize - Q4_K_M offers a good balance of quality and size, fitting comfortably on 2รT4 (32 GB) with room for KV cache
- Downloads last month
- 27
Hardware compatibility
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Base model
trashpanda-org/gemma-4-31b-larkspur-v0.5