Instructions to use laelhalawani/X-ALMA-13B-Group5-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use laelhalawani/X-ALMA-13B-Group5-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf laelhalawani/X-ALMA-13B-Group5-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf laelhalawani/X-ALMA-13B-Group5-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf laelhalawani/X-ALMA-13B-Group5-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf laelhalawani/X-ALMA-13B-Group5-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf laelhalawani/X-ALMA-13B-Group5-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf laelhalawani/X-ALMA-13B-Group5-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf laelhalawani/X-ALMA-13B-Group5-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf laelhalawani/X-ALMA-13B-Group5-GGUF:Q4_K_M

Use Docker

docker model run hf.co/laelhalawani/X-ALMA-13B-Group5-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use laelhalawani/X-ALMA-13B-Group5-GGUF with Ollama:
```
ollama run hf.co/laelhalawani/X-ALMA-13B-Group5-GGUF:Q4_K_M
```

Unsloth Studio

How to use laelhalawani/X-ALMA-13B-Group5-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for laelhalawani/X-ALMA-13B-Group5-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for laelhalawani/X-ALMA-13B-Group5-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for laelhalawani/X-ALMA-13B-Group5-GGUF to start chatting

Docker Model Runner
How to use laelhalawani/X-ALMA-13B-Group5-GGUF with Docker Model Runner:
```
docker model run hf.co/laelhalawani/X-ALMA-13B-Group5-GGUF:Q4_K_M
```

Lemonade

How to use laelhalawani/X-ALMA-13B-Group5-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull laelhalawani/X-ALMA-13B-Group5-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.X-ALMA-13B-Group5-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

X-ALMA-13B-Group5 — GGUF quantized variants

Original upload: X-ALMA-13B-Group5

This upload contains X-ALMA Group 5 (ALMA‑R base + merged LoRA for English (en), Hungarian (hu), Greek (el), Czech (cs), Polish (pl), Lithuanian (lt), and Latvian (lv)), quantized to GGUF format.

Files & sizes

Made with llama.cpp.

File	Size (MB)
X-ALMA-13B-Group5.Q2_K.gguf	4629.39
X-ALMA-13B-Group5.Q3_K_L.gguf	6608.54
X-ALMA-13B-Group5.Q4_K_M.gguf	7501.56
X-ALMA-13B-Group5.Q5_K_M.gguf	8802.34

Prompt format (example)

Translate this from {source_lang} to {target_lang}:
{source_lang}: {text_to_translate}
{target_lang}:

Replace the placeholders with your languages/text (e.g., using Python "..." .format(source_lang="English", target_lang="Polish", text_to_translate="I love machine translation.") or by manually editing the prompt).

Contact me if you'd like a different quant size or quantization of another model — I might be able to help.

Original description

X-ALMA builds upon ALMA-R by expanding support from 6 to 50 languages. It utilizes a plug-and-play architecture with language-specific modules, complemented by a carefully designed training recipe. This release includes the language-specific X-ALMA LoRA module and a merged model that supports the languages in Group 5: English (en), Hungarian (hu), Greek (el), Czech (cs), Polish (pl), Lithuanian (lt), and Latvian (lv).

@misc{xu2024xalmaplugplay,
      title={X-ALMA: Plug & Play Modules and Adaptive Rejection for Quality Translation at Scale}, 
      author={Haoran Xu and Kenton Murray and Philipp Koehn and Hieu Hoang and Akiko Eriguchi and Huda Khayrallah},
      year={2024},
      eprint={2410.03115},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2410.03115}, 
}