How to use from
llama.cppInstall from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf qnixsynapse/Gemma-V2-9B-Instruct-GGUF:Q4_K_S# Run inference directly in the terminal:
llama cli -hf qnixsynapse/Gemma-V2-9B-Instruct-GGUF:Q4_K_SUse 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 qnixsynapse/Gemma-V2-9B-Instruct-GGUF:Q4_K_S# Run inference directly in the terminal:
./llama-cli -hf qnixsynapse/Gemma-V2-9B-Instruct-GGUF:Q4_K_SBuild 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 qnixsynapse/Gemma-V2-9B-Instruct-GGUF:Q4_K_S# Run inference directly in the terminal:
./build/bin/llama-cli -hf qnixsynapse/Gemma-V2-9B-Instruct-GGUF:Q4_K_SUse Docker
docker model run hf.co/qnixsynapse/Gemma-V2-9B-Instruct-GGUF:Q4_K_SQuick Links
Llamacpp Quantizations of official gguf of gemma-2-9b-it from kaggle repo
Using llama.cpp PR 8156 for quantization.
Original model: https://huggingface.co/google/gemma-2-9b-it
Downloading using huggingface-cli
First, make sure you have hugginface-cli installed:
pip install -U "huggingface_hub[cli]"
Then, you can target the specific file you want:
huggingface-cli download qnixsynapse/Gemma-V2-9B-Instruct-GGUF --include "<desired model file name>" --local-dir ./
or you can download directly.
Prompt format
The prompt format is same as Gemma v1 however not included with gguf file. This can be edited with gguf script to add a new key chat_template later.
<bos><start_of_turn>user
{prompt}<end_of_turn>
<start_of_turn>model
The model should stop either at <eos> or <end_of_turn>. If it doesn't then stop tokens needs to be added to the gguf metadata.
Quants
Currently only two quants are available:
| quant | size |
|---|---|
| Q4_K_S | 5.5GB |
| Q3_K_M | 4.8GB |
If Q4_K_S is causing OOM when offloading all the layers to the GPU, consider decreasing batch size or use Q3_K_M.
Minimum VRAM needed: 8GB
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Hardware compatibility
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Install (macOS, Linux)
# Start a local OpenAI-compatible server with a web UI: llama serve -hf qnixsynapse/Gemma-V2-9B-Instruct-GGUF:Q4_K_S# Run inference directly in the terminal: llama cli -hf qnixsynapse/Gemma-V2-9B-Instruct-GGUF:Q4_K_S