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 CoolSpring/gemma-2-9b-it-liaozhai-Q4_K_M-GGUF:Q4_K_M# Run inference directly in the terminal:
llama cli -hf CoolSpring/gemma-2-9b-it-liaozhai-Q4_K_M-GGUF:Q4_K_MUse 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 CoolSpring/gemma-2-9b-it-liaozhai-Q4_K_M-GGUF:Q4_K_M# Run inference directly in the terminal:
./llama-cli -hf CoolSpring/gemma-2-9b-it-liaozhai-Q4_K_M-GGUF:Q4_K_MBuild 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 CoolSpring/gemma-2-9b-it-liaozhai-Q4_K_M-GGUF:Q4_K_M# Run inference directly in the terminal:
./build/bin/llama-cli -hf CoolSpring/gemma-2-9b-it-liaozhai-Q4_K_M-GGUF:Q4_K_MUse Docker
docker model run hf.co/CoolSpring/gemma-2-9b-it-liaozhai-Q4_K_M-GGUF:Q4_K_MQuick Links
Refer to CoolSpring/gemma-2-9b-it-liaozhai for more details.
Prompt Template - gemma
<bos><start_of_turn>user
{input}<end_of_turn>
<start_of_turn>model
{output}<end_of_turn>
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Model tree for CoolSpring/gemma-2-9b-it-liaozhai-Q4_K_M-GGUF
Base model
google/gemma-2-9b Finetuned
google/gemma-2-9b-it Quantized
unsloth/gemma-2-9b-it-bnb-4bit Finetuned
CoolSpring/gemma-2-9b-it-liaozhai
Install (macOS, Linux)
# Start a local OpenAI-compatible server with a web UI: llama serve -hf CoolSpring/gemma-2-9b-it-liaozhai-Q4_K_M-GGUF:Q4_K_M# Run inference directly in the terminal: llama cli -hf CoolSpring/gemma-2-9b-it-liaozhai-Q4_K_M-GGUF:Q4_K_M