File size: 1,780 Bytes
90eeddb |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 |
---
base_model: unsloth/gemma-2-9b
language:
- en
library_name: transformers
license: gemma
tags:
- unsloth
- transformers
- gemma2
- gemma
- llama-cpp
- gguf-my-repo
---
# Bakanayatsu/gemma-2-9b-Q5_K_M-GGUF
This model was converted to GGUF format from [`unsloth/gemma-2-9b`](https://huggingface.co/unsloth/gemma-2-9b) 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) 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 Bakanayatsu/gemma-2-9b-Q5_K_M-GGUF --hf-file gemma-2-9b-q5_k_m-imat.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Bakanayatsu/gemma-2-9b-Q5_K_M-GGUF --hf-file gemma-2-9b-q5_k_m-imat.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 Bakanayatsu/gemma-2-9b-Q5_K_M-GGUF --hf-file gemma-2-9b-q5_k_m-imat.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Bakanayatsu/gemma-2-9b-Q5_K_M-GGUF --hf-file gemma-2-9b-q5_k_m-imat.gguf -c 2048
```
|