Instructions to use csala/ALIA-40b-Q3_K-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use csala/ALIA-40b-Q3_K-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="csala/ALIA-40b-Q3_K-GGUF")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("csala/ALIA-40b-Q3_K-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use csala/ALIA-40b-Q3_K-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 csala/ALIA-40b-Q3_K-GGUF
# Run inference directly in the terminal:
llama cli -hf csala/ALIA-40b-Q3_K-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf csala/ALIA-40b-Q3_K-GGUF
# Run inference directly in the terminal:
llama cli -hf csala/ALIA-40b-Q3_K-GGUF

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 csala/ALIA-40b-Q3_K-GGUF
# Run inference directly in the terminal:
./llama-cli -hf csala/ALIA-40b-Q3_K-GGUF

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 csala/ALIA-40b-Q3_K-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf csala/ALIA-40b-Q3_K-GGUF

Use Docker

docker model run hf.co/csala/ALIA-40b-Q3_K-GGUF

LM Studio
Jan

vLLM

How to use csala/ALIA-40b-Q3_K-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "csala/ALIA-40b-Q3_K-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "csala/ALIA-40b-Q3_K-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/csala/ALIA-40b-Q3_K-GGUF

SGLang

How to use csala/ALIA-40b-Q3_K-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 "csala/ALIA-40b-Q3_K-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "csala/ALIA-40b-Q3_K-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "csala/ALIA-40b-Q3_K-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "csala/ALIA-40b-Q3_K-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use csala/ALIA-40b-Q3_K-GGUF with Ollama:
```
ollama run hf.co/csala/ALIA-40b-Q3_K-GGUF
```

Unsloth Studio

How to use csala/ALIA-40b-Q3_K-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 csala/ALIA-40b-Q3_K-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 csala/ALIA-40b-Q3_K-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for csala/ALIA-40b-Q3_K-GGUF to start chatting

Docker Model Runner
How to use csala/ALIA-40b-Q3_K-GGUF with Docker Model Runner:
```
docker model run hf.co/csala/ALIA-40b-Q3_K-GGUF
```

Lemonade

How to use csala/ALIA-40b-Q3_K-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull csala/ALIA-40b-Q3_K-GGUF

Run and chat with the model

lemonade run user.ALIA-40b-Q3_K-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Atomic Chat

ALIA-40b-Q3_K-GGUF / README.md

csala

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	![image.png](https://huggingface.co/BSC-LT/ALIA-40b/resolve/main/images/logo_alia_2.png)

	# ALIA-40b in GGUF format and quantized to `Q3_K`

	ALIA-40B is a 40B parameter base language model developed by the Barcelona Supercomputing Center (BSC).

	Original model and details here: https://huggingface.co/BSC-LT/ALIA-40b

	This model is released under a permissive [Apache 2.0 license](https://www.apache.org/licenses/LICENSE-2.0).
	Along with the open weights, all training scripts and configuration files are made publicly available in [this GitHub repository](https://github.com/langtech-bsc/alia).

	This repository contains the model in GGUF format and afterwards quantized to `Q3_K` level using `llama.cpp`.

	---

	## Model Details

	### Description

	Transformer-based decoder-only language model that has been pre-trained from scratch on 9.37 trillion tokens of highly curated data.
	The pre-training corpus contains text in 35 European languages and code.

	### Hyperparameters

	The full list of hyperparameters can be found [here](https://github.com/langtech-bsc/alia/blob/main/configs).

	### Architecture

	\| \| \|
	\|-------------------------\|:--------------\|
	\| Total Parameters \| 40,433,885,184\|
	\| Embedding Parameters \| 2,097,152,000 \|
	\| Layers \| 48 \|
	\| Hidden size \| 8,192 \|
	\| Attention heads \| 64 \|
	\| Context length \| 32,768 \|
	\| Vocabulary size \| 256,000 \|
	\| Precision \| bfloat16 \|
	\| Embedding type \| RoPE \|
	\| Activation Function \| SwiGLU \|
	\| Layer normalization \| RMS Norm \|
	\| Flash attention \| ✅ \|
	\| Grouped Query Attention \| ✅ \|
	\| Num. query groups \| 8 \|

	---

	## Conversion Process

	There are the steps that were followed to convert the weights to GGUF format and quantize.

	### 1. Download from HuggingFace

	Requirement: [huggingface_hub](https://pypi.org/project/huggingface-hub/)

	```bash
	huggingface-cli download --cache-dir . BSC-LT/ALIA-40b
	```

	This command downloads the model into the directory `./models--BSC-LT--ALIA-40b/`

	The safetensors files end up inside `./models--BSC-LT--ALIA-40b/snapshots/aa8a4ac7f9e18f3c2ea8ec0cc84e7783cd751ac7/`.

	## 2. Convert Safetensors to GUFF without quantization using llama.cpp

	Requirement: [llama.cpp](https://github.com/ggml-org/llama.cpp) repository and python requirements installed.

	```bash
	cd $LLAMA_PATH
	python convert_hf_to_gguf.py $ALIA_PATH/models--BSC-LT--ALIA-40b/snapshots/aa8a4ac7f9e18f3c2ea8ec0cc84e7783cd751ac7/ --outfile $ALIA_PATH/ALIA-40B.gguf
	```

	`LLAMA_PATH` is the root of the llama.cpp directory.
	`ALIA_PATH` is the directory where we downloaded the Safetensors weights and where we want to store the ALIA-40B GGUF file.

	This creates the file `$ALIA_PATH/ALIA-40B.gguf`.

	## 3. Quantize the model

	Requirement: [llama.cpp](https://github.com/ggml-org/llama.cpp) built and installed.

	```bash
	cd $ALIA_PATH
	llama-quantize ALIA-40B.gguf ALIA-40B.Q3_K.gguf Q3_K
	```

	This generates the file `ALIA-40B.Q3_K.gguf` within the same directory.