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language:
- en
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-2
- llama-cpp
- gguf-my-repo
license: llama2
base_model: meta-llama/Llama-2-7b-hf
---
# seungwon12/Llama-2-7b-hf-Q4_K_M-GGUF
This model was converted to GGUF format from [`meta-llama/Llama-2-7b-hf`](https://huggingface.co/meta-llama/Llama-2-7b-hf) 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/meta-llama/Llama-2-7b-hf) 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 seungwon12/Llama-2-7b-hf-Q4_K_M-GGUF --hf-file llama-2-7b-hf-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo seungwon12/Llama-2-7b-hf-Q4_K_M-GGUF --hf-file llama-2-7b-hf-q4_k_m.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 seungwon12/Llama-2-7b-hf-Q4_K_M-GGUF --hf-file llama-2-7b-hf-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo seungwon12/Llama-2-7b-hf-Q4_K_M-GGUF --hf-file llama-2-7b-hf-q4_k_m.gguf -c 2048
```