README.md

---
base_model: google/gemma-2-9b
library_name: transformers
license: other
tags:
- llama-cpp
- gemma
- gemma-2-9b
- gemma-9b
- GGUF
---

# Sri-Vigneshwar-DJ/gemma-2-9b-marketing-content-GGUF
This model was converted to GGUF format from [`google/gemma-2-9b`](https://huggingface.co/google/gemma-2-9b) using llama.cpp
Refer to the [original model card](https://huggingface.co/google/gemma-2-9b) for more details on the model.

## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux) from []

```bash
brew install llama.cpp or !git clone https://github.com/ggerganov/llama.cpp.git

```
Invoke the llama.cpp server or the CLI.

### CLI:
```bash
! /content/llama.cpp/llama-cli -m ./sarvam-2b-v0.5-GGUF -n 90 --repeat_penalty 1.0 --color -i -r "User:" -f /content/llama.cpp/prompts/chat-with-bob.txt

or

llama-cli --hf-repo Sri-Vigneshwar-DJ/sarvam-2b-v0.5-GGUF --hf-file FP8.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Sri-Vigneshwar-DJ/gemma-2-9b-marketing-content-GGUF --hf-file FP8.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 or ''!make GGML_OPENBLAS=1' along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make

or

!make GGML_OPENBLAS=1
```

Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Sri-Vigneshwar-DJ/gemma-2-9b-marketing-content-GGUF --hf-file FP8.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Sri-Vigneshwar-DJ/gemma-2-9b-marketing-content-GGUF --hf-file sFP8.gguf -c 2048
```