README.md

---
license: apache-2.0
language:
- multilingual
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library_name: transformers
tags:
- text2text-generation
- text-generation-inference
- llama-cpp
datasets:
- allenai/MADLAD-400
pipeline_tag: translation
widget:
- text: <2en> Como vai, amigo?
  example_title: Translation to English
- text: <2de> Do you speak German?
  example_title: Translation to German
base_model: google/madlad400-10b-mt
---

# Eddishockwave/madlad400-10b-mt-Q8_0-GGUF
This model was converted to GGUF format from [`google/madlad400-10b-mt`](https://huggingface.co/google/madlad400-10b-mt) using llama.cpp
Refer to the [original model card](https://huggingface.co/google/madlad400-10b-mt) 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 Eddishockwave/madlad400-10b-mt-Q8_0-GGUF --hf-file madlad400-10b-mt-q8_0.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Eddishockwave/madlad400-10b-mt-Q8_0-GGUF --hf-file madlad400-10b-mt-q8_0.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 Eddishockwave/madlad400-10b-mt-Q8_0-GGUF --hf-file madlad400-10b-mt-q8_0.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Eddishockwave/madlad400-10b-mt-Q8_0-GGUF --hf-file madlad400-10b-mt-q8_0.gguf -c 2048
```