Instructions to use layerx-labs/AMALIA-9B-0626-SFT-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use layerx-labs/AMALIA-9B-0626-SFT-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="layerx-labs/AMALIA-9B-0626-SFT-GGUF",
	filename="AMALIA-9B-0626-SFT-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use layerx-labs/AMALIA-9B-0626-SFT-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 layerx-labs/AMALIA-9B-0626-SFT-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf layerx-labs/AMALIA-9B-0626-SFT-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 layerx-labs/AMALIA-9B-0626-SFT-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf layerx-labs/AMALIA-9B-0626-SFT-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 layerx-labs/AMALIA-9B-0626-SFT-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf layerx-labs/AMALIA-9B-0626-SFT-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 layerx-labs/AMALIA-9B-0626-SFT-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf layerx-labs/AMALIA-9B-0626-SFT-GGUF:Q4_K_M

Use Docker

docker model run hf.co/layerx-labs/AMALIA-9B-0626-SFT-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use layerx-labs/AMALIA-9B-0626-SFT-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "layerx-labs/AMALIA-9B-0626-SFT-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "layerx-labs/AMALIA-9B-0626-SFT-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/layerx-labs/AMALIA-9B-0626-SFT-GGUF:Q4_K_M

Ollama
How to use layerx-labs/AMALIA-9B-0626-SFT-GGUF with Ollama:
```
ollama run hf.co/layerx-labs/AMALIA-9B-0626-SFT-GGUF:Q4_K_M
```

Unsloth Studio

How to use layerx-labs/AMALIA-9B-0626-SFT-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 layerx-labs/AMALIA-9B-0626-SFT-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 layerx-labs/AMALIA-9B-0626-SFT-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for layerx-labs/AMALIA-9B-0626-SFT-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use layerx-labs/AMALIA-9B-0626-SFT-GGUF with Docker Model Runner:
```
docker model run hf.co/layerx-labs/AMALIA-9B-0626-SFT-GGUF:Q4_K_M
```

Lemonade

How to use layerx-labs/AMALIA-9B-0626-SFT-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull layerx-labs/AMALIA-9B-0626-SFT-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.AMALIA-9B-0626-SFT-GGUF-Q4_K_M

List all available models

lemonade list

AMALIA-9B-0626-SFT — GGUF

GGUF quantizations of amalia-llm/AMALIA-9B-0626-SFT for use with llama.cpp and compatible runtimes (LM Studio, Ollama, Jan, etc.).

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 SFT (supervised fine-tuned, instruction-following) variant. See the original model card and the technical report for full details.

Files

File	Quant	Size	Notes
`AMALIA-9B-0626-SFT-Q4_K_M.gguf`	Q4_K_M	~5.5 GB	4-bit, recommended balance of quality and size
`AMALIA-9B-0626-SFT-Q8_0.gguf`	Q8_0	~9.7 GB	8-bit, near-lossless

Converted with convert_hf_to_gguf.py (llama.cpp b8470) from the original bf16 weights to Q8_0; Q4_K_M requantized from the Q8_0 conversion (Q8_0 is near-lossless relative to bf16). The chat template (ChatML with the AMALIA Portuguese system prompt) is embedded in the GGUF metadata.

Usage

# llama.cpp CLI (chat template is picked up automatically)
llama-cli -hf layerx-labs/AMALIA-9B-0626-SFT-GGUF:Q4_K_M

# or with a local file
llama-cli -m AMALIA-9B-0626-SFT-Q4_K_M.gguf -cnv

# OpenAI-compatible server
llama-server -m AMALIA-9B-0626-SFT-Q4_K_M.gguf -c 8192

License

Apache 2.0, same as the original model. All credit for the model goes to the AMALIA project.

Downloads last month: 121

GGUF

Model size

9B params

Architecture

llama

Hardware compatibility

4-bit

8-bit

Model tree for layerx-labs/AMALIA-9B-0626-SFT-GGUF

Base model

amalia-llm/AMALIA-9B-0626-SFT

Quantized

(8)

this model

Paper for layerx-labs/AMALIA-9B-0626-SFT-GGUF

AMALIA Technical Report: A Fully Open Source Large Language Model for European Portuguese

Paper • 2603.26511 • Published Mar 27