Instructions to use NikolayKozloff/Llama-portuguese-13b-Luana-v0.2-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NikolayKozloff/Llama-portuguese-13b-Luana-v0.2-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="NikolayKozloff/Llama-portuguese-13b-Luana-v0.2-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("NikolayKozloff/Llama-portuguese-13b-Luana-v0.2-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use NikolayKozloff/Llama-portuguese-13b-Luana-v0.2-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 NikolayKozloff/Llama-portuguese-13b-Luana-v0.2-GGUF:Q6_K
# Run inference directly in the terminal:
llama cli -hf NikolayKozloff/Llama-portuguese-13b-Luana-v0.2-GGUF:Q6_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf NikolayKozloff/Llama-portuguese-13b-Luana-v0.2-GGUF:Q6_K
# Run inference directly in the terminal:
llama cli -hf NikolayKozloff/Llama-portuguese-13b-Luana-v0.2-GGUF:Q6_K

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 NikolayKozloff/Llama-portuguese-13b-Luana-v0.2-GGUF:Q6_K
# Run inference directly in the terminal:
./llama-cli -hf NikolayKozloff/Llama-portuguese-13b-Luana-v0.2-GGUF:Q6_K

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 NikolayKozloff/Llama-portuguese-13b-Luana-v0.2-GGUF:Q6_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf NikolayKozloff/Llama-portuguese-13b-Luana-v0.2-GGUF:Q6_K

Use Docker

docker model run hf.co/NikolayKozloff/Llama-portuguese-13b-Luana-v0.2-GGUF:Q6_K

LM Studio
Jan

vLLM

How to use NikolayKozloff/Llama-portuguese-13b-Luana-v0.2-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NikolayKozloff/Llama-portuguese-13b-Luana-v0.2-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": "NikolayKozloff/Llama-portuguese-13b-Luana-v0.2-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/NikolayKozloff/Llama-portuguese-13b-Luana-v0.2-GGUF:Q6_K

SGLang

How to use NikolayKozloff/Llama-portuguese-13b-Luana-v0.2-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 "NikolayKozloff/Llama-portuguese-13b-Luana-v0.2-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": "NikolayKozloff/Llama-portuguese-13b-Luana-v0.2-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 "NikolayKozloff/Llama-portuguese-13b-Luana-v0.2-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": "NikolayKozloff/Llama-portuguese-13b-Luana-v0.2-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use NikolayKozloff/Llama-portuguese-13b-Luana-v0.2-GGUF with Ollama:
```
ollama run hf.co/NikolayKozloff/Llama-portuguese-13b-Luana-v0.2-GGUF:Q6_K
```

Unsloth Studio

How to use NikolayKozloff/Llama-portuguese-13b-Luana-v0.2-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 NikolayKozloff/Llama-portuguese-13b-Luana-v0.2-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 NikolayKozloff/Llama-portuguese-13b-Luana-v0.2-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for NikolayKozloff/Llama-portuguese-13b-Luana-v0.2-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use NikolayKozloff/Llama-portuguese-13b-Luana-v0.2-GGUF with Docker Model Runner:
```
docker model run hf.co/NikolayKozloff/Llama-portuguese-13b-Luana-v0.2-GGUF:Q6_K
```

Lemonade

How to use NikolayKozloff/Llama-portuguese-13b-Luana-v0.2-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull NikolayKozloff/Llama-portuguese-13b-Luana-v0.2-GGUF:Q6_K

Run and chat with the model

lemonade run user.Llama-portuguese-13b-Luana-v0.2-GGUF-Q6_K

List all available models

lemonade list

NikolayKozloff/Llama-portuguese-13b-Luana-v0.2-Q6_K-GGUF

This model was converted to GGUF format from rhaymison/Llama-portuguese-13b-Luana-v0.2 using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

Use with llama.cpp

Install llama.cpp through brew.

brew install ggerganov/ggerganov/llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo NikolayKozloff/Llama-portuguese-13b-Luana-v0.2-Q6_K-GGUF --model llama-portuguese-13b-luana-v0.2.Q6_K.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo NikolayKozloff/Llama-portuguese-13b-Luana-v0.2-Q6_K-GGUF --model llama-portuguese-13b-luana-v0.2.Q6_K.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

git clone https://github.com/ggerganov/llama.cpp &&             cd llama.cpp &&             make &&             ./main -m llama-portuguese-13b-luana-v0.2.Q6_K.gguf -n 128

Downloads last month: 51

GGUF

Model size

13B params

Architecture

llama

Hardware compatibility

6-bit

Model tree for NikolayKozloff/Llama-portuguese-13b-Luana-v0.2-GGUF

Base model

meta-llama/Llama-2-13b-chat-hf

Quantized

(21)

this model

Datasets used to train NikolayKozloff/Llama-portuguese-13b-Luana-v0.2-GGUF

Evaluation results

accuracy on ENEM Challenge (No Images)
Open Portuguese LLM Leaderboard

36.950
accuracy on BLUEX (No Images)
Open Portuguese LLM Leaderboard

32.680
accuracy on OAB Exams
Open Portuguese LLM Leaderboard

33.300
f1-macro on Assin2 RTE
test set Open Portuguese LLM Leaderboard

65.830
pearson on Assin2 STS
test set Open Portuguese LLM Leaderboard

42.810
f1-macro on FaQuAD NLI
test set Open Portuguese LLM Leaderboard

40.440
f1-macro on HateBR Binary
test set Open Portuguese LLM Leaderboard

83.620
f1-macro on PT Hate Speech Binary
test set Open Portuguese LLM Leaderboard

54.620