---
language:
- eng
license:
- mit
tags:
- sft
- StableLM
- llama-cpp
- gguf-my-repo
base_model: NousResearch/Nous-Capybara-7B-V1.9
datasets:
- LDJnr/Capybara
- LDJnr/LessWrong-Amplify-Instruct
- LDJnr/Pure-Dove
- LDJnr/Verified-Camel
---

# SixOpen/Nous-Capybara-7B-V1.9-IQ4_NL-GGUF
This model was converted to GGUF format from [`NousResearch/Nous-Capybara-7B-V1.9`](https://huggingface.co/NousResearch/Nous-Capybara-7B-V1.9) 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/NousResearch/Nous-Capybara-7B-V1.9) 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 --hf-repo SixOpen/Nous-Capybara-7B-V1.9-IQ4_NL-GGUF --hf-file nous-capybara-7b-v1.9-iq4_nl-imat.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo SixOpen/Nous-Capybara-7B-V1.9-IQ4_NL-GGUF --hf-file nous-capybara-7b-v1.9-iq4_nl-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.
```
./main --hf-repo SixOpen/Nous-Capybara-7B-V1.9-IQ4_NL-GGUF --hf-file nous-capybara-7b-v1.9-iq4_nl-imat.gguf -p "The meaning to life and the universe is"
```
or 
```
./server --hf-repo SixOpen/Nous-Capybara-7B-V1.9-IQ4_NL-GGUF --hf-file nous-capybara-7b-v1.9-iq4_nl-imat.gguf -c 2048
```