Instructions to use BSC-LT/ALIA-40b-fc-2606-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BSC-LT/ALIA-40b-fc-2606-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="BSC-LT/ALIA-40b-fc-2606-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("BSC-LT/ALIA-40b-fc-2606-GGUF", device_map="auto")

llama-cpp-python

How to use BSC-LT/ALIA-40b-fc-2606-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="BSC-LT/ALIA-40b-fc-2606-GGUF",
	filename="ALIA-40b-fc-2606-Q8_0.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 BSC-LT/ALIA-40b-fc-2606-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 BSC-LT/ALIA-40b-fc-2606-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf BSC-LT/ALIA-40b-fc-2606-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf BSC-LT/ALIA-40b-fc-2606-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf BSC-LT/ALIA-40b-fc-2606-GGUF:Q8_0

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 BSC-LT/ALIA-40b-fc-2606-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf BSC-LT/ALIA-40b-fc-2606-GGUF:Q8_0

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 BSC-LT/ALIA-40b-fc-2606-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf BSC-LT/ALIA-40b-fc-2606-GGUF:Q8_0

Use Docker

docker model run hf.co/BSC-LT/ALIA-40b-fc-2606-GGUF:Q8_0

LM Studio
Jan

vLLM

How to use BSC-LT/ALIA-40b-fc-2606-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BSC-LT/ALIA-40b-fc-2606-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": "BSC-LT/ALIA-40b-fc-2606-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/BSC-LT/ALIA-40b-fc-2606-GGUF:Q8_0

SGLang

How to use BSC-LT/ALIA-40b-fc-2606-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "BSC-LT/ALIA-40b-fc-2606-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BSC-LT/ALIA-40b-fc-2606-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "BSC-LT/ALIA-40b-fc-2606-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BSC-LT/ALIA-40b-fc-2606-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use BSC-LT/ALIA-40b-fc-2606-GGUF with Ollama:
```
ollama run hf.co/BSC-LT/ALIA-40b-fc-2606-GGUF:Q8_0
```

Unsloth Studio

How to use BSC-LT/ALIA-40b-fc-2606-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 BSC-LT/ALIA-40b-fc-2606-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 BSC-LT/ALIA-40b-fc-2606-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for BSC-LT/ALIA-40b-fc-2606-GGUF to start chatting

How to use BSC-LT/ALIA-40b-fc-2606-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf BSC-LT/ALIA-40b-fc-2606-GGUF:Q8_0

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "BSC-LT/ALIA-40b-fc-2606-GGUF:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use BSC-LT/ALIA-40b-fc-2606-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf BSC-LT/ALIA-40b-fc-2606-GGUF:Q8_0

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default BSC-LT/ALIA-40b-fc-2606-GGUF:Q8_0

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use BSC-LT/ALIA-40b-fc-2606-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf BSC-LT/ALIA-40b-fc-2606-GGUF:Q8_0

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "BSC-LT/ALIA-40b-fc-2606-GGUF:Q8_0" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use BSC-LT/ALIA-40b-fc-2606-GGUF with Docker Model Runner:
```
docker model run hf.co/BSC-LT/ALIA-40b-fc-2606-GGUF:Q8_0
```

Lemonade

How to use BSC-LT/ALIA-40b-fc-2606-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull BSC-LT/ALIA-40b-fc-2606-GGUF:Q8_0

Run and chat with the model

lemonade run user.ALIA-40b-fc-2606-GGUF-Q8_0

List all available models

lemonade list

ALIA-40b-fc-GGUF

Model creator: BSC-LT.
Original model: ALIA-40b-fc-2606

Description

This repo contains GGUF format model files for ALIA-40b-fc-2606.

Quantization

Model weights were exported to GGUF in FP16 first, then quantized with llama.cpp’s llama-quantize into the target preset (e.g., Q8). The same conversion + quantization pipeline was executed through quantool from a single YAML config (e.g., method: gguf, quant_level, and method-specific quantization_config) and run via quantool config.yaml.

About GGUF

GGUF is the model file format introduced by the llama.cpp team on August 21st, 2023, replacing the older GGML format (now deprecated). It brings significant improvements such as enhanced tokenization, proper handling of special tokens, embedded metadata (e.g., architecture, quantization type, tokenizer), and an extensible design for future compatibility.

Additional information

Author

The Language Modeling team from AI Institute at Barcelona Supercomputing Center.

Contact

For further information, please send an email to ai_institute_languagemodeling@bsc.es.

Copyright

Funding

This work is funded by the Ministerio para la Transformación Digital y de la Función Pública - Funded by EU – NextGenerationEU within the framework of the project Modelos del Lenguaje.

This work has been promoted and supported by the Government of Catalonia through the Aina Project.

Disclaimer

Be aware that the model may contain biases or other unintended distortions. When third parties deploy systems or provide services based on this model, or use the model themselves, they bear the responsibility for mitigating any associated risks and ensuring compliance with applicable regulations, including those governing the use of Artificial Intelligence.

The Barcelona Supercomputing Center, as the owner and creator of the model, shall not be held liable for any outcomes resulting from third-party use.

Citation

@misc{gonzalezagirre2025salamandratechnicalreport,
      title={Salamandra Technical Report}, 
      author={Aitor Gonzalez-Agirre and Marc Pàmies and Joan Llop and Irene Baucells and Severino Da Dalt and Daniel Tamayo and José Javier Saiz and Ferran Espuña and Jaume Prats and Javier Aula-Blasco and Mario Mina and Adrián Rubio and Alexander Shvets and Anna Sallés and Iñaki Lacunza and Iñigo Pikabea and Jorge Palomar and Júlia Falcão and Lucía Tormo and Luis Vasquez-Reina and Montserrat Marimon and Valle Ruíz-Fernández and Marta Villegas},
      year={2025},
      eprint={2502.08489},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2502.08489}, 
}