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---
language:
- en
library_name: transformers
license: gemma
tags:
- unsloth
- transformers
- gemma2
- gemma
- llama-cpp
- gguf-my-repo
base_model: unsloth/gemma-2-9b-it
---
# Triangle104/gemma-2-9b-it-Q4_K_S-GGUF
This model was converted to GGUF format from [`unsloth/gemma-2-9b-it`](https://huggingface.co/unsloth/gemma-2-9b-it) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/unsloth/gemma-2-9b-it) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Triangle104/gemma-2-9b-it-Q4_K_S-GGUF --hf-file gemma-2-9b-it-q4_k_s.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/gemma-2-9b-it-Q4_K_S-GGUF --hf-file gemma-2-9b-it-q4_k_s.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/gemma-2-9b-it-Q4_K_S-GGUF --hf-file gemma-2-9b-it-q4_k_s.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/gemma-2-9b-it-Q4_K_S-GGUF --hf-file gemma-2-9b-it-q4_k_s.gguf -c 2048
```
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