Instructions to use fernandoruiz/ALIA-40b-Q4_0-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use fernandoruiz/ALIA-40b-Q4_0-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="fernandoruiz/ALIA-40b-Q4_0-GGUF")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("fernandoruiz/ALIA-40b-Q4_0-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use fernandoruiz/ALIA-40b-Q4_0-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 fernandoruiz/ALIA-40b-Q4_0-GGUF:Q4_0
# Run inference directly in the terminal:
llama cli -hf fernandoruiz/ALIA-40b-Q4_0-GGUF:Q4_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf fernandoruiz/ALIA-40b-Q4_0-GGUF:Q4_0
# Run inference directly in the terminal:
llama cli -hf fernandoruiz/ALIA-40b-Q4_0-GGUF:Q4_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 fernandoruiz/ALIA-40b-Q4_0-GGUF:Q4_0
# Run inference directly in the terminal:
./llama-cli -hf fernandoruiz/ALIA-40b-Q4_0-GGUF:Q4_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 fernandoruiz/ALIA-40b-Q4_0-GGUF:Q4_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf fernandoruiz/ALIA-40b-Q4_0-GGUF:Q4_0

Use Docker

docker model run hf.co/fernandoruiz/ALIA-40b-Q4_0-GGUF:Q4_0

LM Studio
Jan

vLLM

How to use fernandoruiz/ALIA-40b-Q4_0-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "fernandoruiz/ALIA-40b-Q4_0-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fernandoruiz/ALIA-40b-Q4_0-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/fernandoruiz/ALIA-40b-Q4_0-GGUF:Q4_0

SGLang

How to use fernandoruiz/ALIA-40b-Q4_0-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 "fernandoruiz/ALIA-40b-Q4_0-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": "fernandoruiz/ALIA-40b-Q4_0-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 "fernandoruiz/ALIA-40b-Q4_0-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": "fernandoruiz/ALIA-40b-Q4_0-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use fernandoruiz/ALIA-40b-Q4_0-GGUF with Ollama:
```
ollama run hf.co/fernandoruiz/ALIA-40b-Q4_0-GGUF:Q4_0
```

Unsloth Studio

How to use fernandoruiz/ALIA-40b-Q4_0-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 fernandoruiz/ALIA-40b-Q4_0-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 fernandoruiz/ALIA-40b-Q4_0-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for fernandoruiz/ALIA-40b-Q4_0-GGUF to start chatting

Docker Model Runner
How to use fernandoruiz/ALIA-40b-Q4_0-GGUF with Docker Model Runner:
```
docker model run hf.co/fernandoruiz/ALIA-40b-Q4_0-GGUF:Q4_0
```

Lemonade

How to use fernandoruiz/ALIA-40b-Q4_0-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull fernandoruiz/ALIA-40b-Q4_0-GGUF:Q4_0

Run and chat with the model

lemonade run user.ALIA-40b-Q4_0-GGUF-Q4_0

List all available models

lemonade list

Atomic Chat

ALIA-40b-Q4_0-GGUF / README.md

fernandoruiz

Upload README.md with huggingface_hub

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	---
	license: apache-2.0
	library_name: transformers
	pipeline_tag: text-generation
	language:
	- bg
	- ca
	- code
	- cs
	- cy
	- da
	- de
	- el
	- en
	- es
	- et
	- eu
	- fi
	- fr
	- ga
	- gl
	- hr
	- hu
	- it
	- lt
	- lv
	- mt
	- nl
	- nn
	- \no
	- oc
	- pl
	- pt
	- ro
	- ru
	- sh
	- sk
	- sl
	- sr
	- sv
	- uk
	datasets:
	- oscar-corpus/colossal-oscar-1.0
	- HuggingFaceFW/fineweb-edu
	- joelniklaus/eurlex_resources
	- joelniklaus/legal-mc4
	- projecte-aina/CATalog
	- UFRGS/brwac
	- community-datasets/hrwac
	- danish-foundation-models/danish-gigaword
	- HiTZ/euscrawl
	- PleIAs/French-PD-Newspapers
	- PleIAs/French-PD-Books
	- AI-team-UoA/greek_legal_code
	- HiTZ/latxa-corpus-v1.1
	- allenai/peS2o
	- pile-of-law/pile-of-law
	- PORTULAN/parlamento-pt
	- hoskinson-center/proof-pile
	- togethercomputer/RedPajama-Data-1T
	- bigcode/starcoderdata
	- bjoernp/tagesschau-2018-2023
	- EleutherAI/the_pile_deduplicated
	base_model: BSC-LT/ALIA-40b
	tags:
	- llama-cpp
	- gguf-my-repo
	---

	# fernandoruiz/ALIA-40b-Q4_0-GGUF
	This model was converted to GGUF format from [`BSC-LT/ALIA-40b`](https://huggingface.co/BSC-LT/ALIA-40b) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
	Refer to the [original model card](https://huggingface.co/BSC-LT/ALIA-40b) for more details on the model.

	## Use with llama.cpp
	Install llama.cpp through brew (works on Mac and Linux)

	```bash
	brew install llama.cpp

	```
	Invoke the llama.cpp server or the CLI.

	### CLI:
	```bash
	llama-cli --hf-repo fernandoruiz/ALIA-40b-Q4_0-GGUF --hf-file alia-40b-q4_0.gguf -p "The meaning to life and the universe is"
	```

	### Server:
	```bash
	llama-server --hf-repo fernandoruiz/ALIA-40b-Q4_0-GGUF --hf-file alia-40b-q4_0.gguf -c 2048
	```

	Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.

	Step 1: Clone llama.cpp from GitHub.
	```
	git clone https://github.com/ggerganov/llama.cpp
	```

	Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
	```
	cd llama.cpp && LLAMA_CURL=1 make
	```

	Step 3: Run inference through the main binary.
	```
	./llama-cli --hf-repo fernandoruiz/ALIA-40b-Q4_0-GGUF --hf-file alia-40b-q4_0.gguf -p "The meaning to life and the universe is"
	```
	or
	```
	./llama-server --hf-repo fernandoruiz/ALIA-40b-Q4_0-GGUF --hf-file alia-40b-q4_0.gguf -c 2048
	```