---
base_model: Tower-Babel/Babel-9B-Chat
language:
- en
- zh
- hi
- es
- fr
- ar
- bn
- ru
- pt
- id
- ur
- de
- ja
- sw
- ta
- tr
- ko
- vi
- jv
- it
- ha
- th
- fa
- tl
- my
license: other
license_name: seallm
license_link: https://huggingface.co/SeaLLMs/SeaLLM-13B-Chat/blob/main/LICENSE
tags:
- multilingual
- babel
- llama-cpp
- gguf-my-repo
---

# Triangle104/Babel-9B-Chat-Q5_K_S-GGUF
This model was converted to GGUF format from [`Tower-Babel/Babel-9B-Chat`](https://huggingface.co/Tower-Babel/Babel-9B-Chat) 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/Tower-Babel/Babel-9B-Chat) for more details on the model.

---
We introduce Babel, a multilingual LLM that covers the top 25 languages by number of speakers, including English, Chinese, Hindi, Spanish, Arabic, French, Bengali, Portuguese, Russian, Urdu, Indonesian, German, Japanese, Swahili, Filipino, Tamil, Vietnamese, Turkish, Italian, Javanese, Korean, Hausa, Persian, Thai, and Burmese. These 25 languages support over 90% of the global population, and include many languages neglected by other open multilingual LLMs. Unlike traditional continued pretraining approaches, Babel expands its parameter count through a layer extension technique that elevates Babel's performance ceiling.

---
## 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/Babel-9B-Chat-Q5_K_S-GGUF --hf-file babel-9b-chat-q5_k_s.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/Babel-9B-Chat-Q5_K_S-GGUF --hf-file babel-9b-chat-q5_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/Babel-9B-Chat-Q5_K_S-GGUF --hf-file babel-9b-chat-q5_k_s.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/Babel-9B-Chat-Q5_K_S-GGUF --hf-file babel-9b-chat-q5_k_s.gguf -c 2048
```