AMALIA Technical Report: A Fully Open Source Large Language Model for European Portuguese
Paper • 2603.26511 • Published
How to use layerx-labs/AMALIA-9B-0626-DPO-MLX-8bit with MLX:
# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm
# Generate text with mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("layerx-labs/AMALIA-9B-0626-DPO-MLX-8bit")
prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
text = generate(model, tokenizer, prompt=prompt, verbose=True)How to use layerx-labs/AMALIA-9B-0626-DPO-MLX-8bit with MLX LM:
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "layerx-labs/AMALIA-9B-0626-DPO-MLX-8bit"
# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "layerx-labs/AMALIA-9B-0626-DPO-MLX-8bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "layerx-labs/AMALIA-9B-0626-DPO-MLX-8bit",
"messages": [
{"role": "user", "content": "Hello"}
]
}'8-bit MLX quantization of amalia-llm/AMALIA-9B-0626-DPO (~9.7 GB), for Apple Silicon Macs via mlx-lm.
AMALIA is an open-source language model targeting European Portuguese, developed by a consortium of Portuguese universities and research centres and funded by the Government of Portugal. This is the DPO (preference-tuned) variant. See the original model card and the technical report for full details.
Converted with mlx_lm.convert (8-bit, group size 64).
pip install mlx-lm
mlx_lm.generate --model layerx-labs/AMALIA-9B-0626-DPO-MLX-8bit --prompt "Explica o que é o Arquivo.pt."
# or chat
mlx_lm.chat --model layerx-labs/AMALIA-9B-0626-DPO-MLX-8bit
from mlx_lm import load, generate
model, tokenizer = load("layerx-labs/AMALIA-9B-0626-DPO-MLX-8bit")
prompt = tokenizer.apply_chat_template([{"role": "user", "content": "Explica o que é o Arquivo.pt."}], add_generation_prompt=True, tokenize=False)
print(generate(model, tokenizer, prompt=prompt, max_tokens=256))
Apache 2.0, same as the original model. All credit for the model goes to the AMALIA project.
8-bit
Base model
amalia-llm/AMALIA-9B-0626-SFT