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", dtype="auto")

llama-cpp-python

How to use fernandoruiz/ALIA-40b-Q4_0-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="fernandoruiz/ALIA-40b-Q4_0-GGUF",
	filename="alia-40b-q4_0.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

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

Atomic Chat new
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

ALIA-40b-Q4_0-GGUF

File size: 2,474 Bytes

a7518d7

---
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
```