Instructions to use noxinc/Mistral-portuguese-luana-7b-Q5_K_M-GGUF-PTBR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use noxinc/Mistral-portuguese-luana-7b-Q5_K_M-GGUF-PTBR with Transformers:

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

pipe = pipeline("text-generation", model="noxinc/Mistral-portuguese-luana-7b-Q5_K_M-GGUF-PTBR")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("noxinc/Mistral-portuguese-luana-7b-Q5_K_M-GGUF-PTBR", dtype="auto")

llama-cpp-python

How to use noxinc/Mistral-portuguese-luana-7b-Q5_K_M-GGUF-PTBR with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="noxinc/Mistral-portuguese-luana-7b-Q5_K_M-GGUF-PTBR",
	filename="mistral-portuguese-luana-7b.Q5_K_M.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 noxinc/Mistral-portuguese-luana-7b-Q5_K_M-GGUF-PTBR with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf noxinc/Mistral-portuguese-luana-7b-Q5_K_M-GGUF-PTBR:Q5_K_M
# Run inference directly in the terminal:
llama-cli -hf noxinc/Mistral-portuguese-luana-7b-Q5_K_M-GGUF-PTBR:Q5_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf noxinc/Mistral-portuguese-luana-7b-Q5_K_M-GGUF-PTBR:Q5_K_M
# Run inference directly in the terminal:
llama-cli -hf noxinc/Mistral-portuguese-luana-7b-Q5_K_M-GGUF-PTBR:Q5_K_M

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 noxinc/Mistral-portuguese-luana-7b-Q5_K_M-GGUF-PTBR:Q5_K_M
# Run inference directly in the terminal:
./llama-cli -hf noxinc/Mistral-portuguese-luana-7b-Q5_K_M-GGUF-PTBR:Q5_K_M

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 noxinc/Mistral-portuguese-luana-7b-Q5_K_M-GGUF-PTBR:Q5_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf noxinc/Mistral-portuguese-luana-7b-Q5_K_M-GGUF-PTBR:Q5_K_M

Use Docker

docker model run hf.co/noxinc/Mistral-portuguese-luana-7b-Q5_K_M-GGUF-PTBR:Q5_K_M

LM Studio
Jan

vLLM

How to use noxinc/Mistral-portuguese-luana-7b-Q5_K_M-GGUF-PTBR with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "noxinc/Mistral-portuguese-luana-7b-Q5_K_M-GGUF-PTBR"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "noxinc/Mistral-portuguese-luana-7b-Q5_K_M-GGUF-PTBR",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/noxinc/Mistral-portuguese-luana-7b-Q5_K_M-GGUF-PTBR:Q5_K_M

SGLang

How to use noxinc/Mistral-portuguese-luana-7b-Q5_K_M-GGUF-PTBR 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 "noxinc/Mistral-portuguese-luana-7b-Q5_K_M-GGUF-PTBR" \
    --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": "noxinc/Mistral-portuguese-luana-7b-Q5_K_M-GGUF-PTBR",
		"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 "noxinc/Mistral-portuguese-luana-7b-Q5_K_M-GGUF-PTBR" \
        --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": "noxinc/Mistral-portuguese-luana-7b-Q5_K_M-GGUF-PTBR",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use noxinc/Mistral-portuguese-luana-7b-Q5_K_M-GGUF-PTBR with Ollama:
```
ollama run hf.co/noxinc/Mistral-portuguese-luana-7b-Q5_K_M-GGUF-PTBR:Q5_K_M
```

Unsloth Studio

How to use noxinc/Mistral-portuguese-luana-7b-Q5_K_M-GGUF-PTBR 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 noxinc/Mistral-portuguese-luana-7b-Q5_K_M-GGUF-PTBR 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 noxinc/Mistral-portuguese-luana-7b-Q5_K_M-GGUF-PTBR to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for noxinc/Mistral-portuguese-luana-7b-Q5_K_M-GGUF-PTBR to start chatting

Atomic Chat new
Docker Model Runner
How to use noxinc/Mistral-portuguese-luana-7b-Q5_K_M-GGUF-PTBR with Docker Model Runner:
```
docker model run hf.co/noxinc/Mistral-portuguese-luana-7b-Q5_K_M-GGUF-PTBR:Q5_K_M
```

Lemonade

How to use noxinc/Mistral-portuguese-luana-7b-Q5_K_M-GGUF-PTBR with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull noxinc/Mistral-portuguese-luana-7b-Q5_K_M-GGUF-PTBR:Q5_K_M

Run and chat with the model

lemonade run user.Mistral-portuguese-luana-7b-Q5_K_M-GGUF-PTBR-Q5_K_M

List all available models

lemonade list

Mistral-portuguese-luana-7b-Q5_K_M-GGUF-PTBR / README.md

noxinc

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metadata

language:
  - pt
license: apache-2.0
library_name: transformers
tags:
  - Misral
  - Portuguese
  - 7b
  - llama-cpp
  - gguf-my-repo
base_model: mistralai/Mistral-7B-Instruct-v0.2
datasets:
  - pablo-moreira/gpt4all-j-prompt-generations-pt
  - rhaymison/superset
pipeline_tag: text-generation
model-index:
  - name: Mistral-portuguese-luana-7b
    results:
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: ENEM Challenge (No Images)
          type: eduagarcia/enem_challenge
          split: train
          args:
            num_few_shot: 3
        metrics:
          - type: acc
            value: 58.64
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b
          name: Open Portuguese LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: BLUEX (No Images)
          type: eduagarcia-temp/BLUEX_without_images
          split: train
          args:
            num_few_shot: 3
        metrics:
          - type: acc
            value: 47.98
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b
          name: Open Portuguese LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: OAB Exams
          type: eduagarcia/oab_exams
          split: train
          args:
            num_few_shot: 3
        metrics:
          - type: acc
            value: 38.82
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b
          name: Open Portuguese LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: Assin2 RTE
          type: assin2
          split: test
          args:
            num_few_shot: 15
        metrics:
          - type: f1_macro
            value: 90.63
            name: f1-macro
        source:
          url: >-
            https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b
          name: Open Portuguese LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: Assin2 STS
          type: eduagarcia/portuguese_benchmark
          split: test
          args:
            num_few_shot: 15
        metrics:
          - type: pearson
            value: 75.81
            name: pearson
        source:
          url: >-
            https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b
          name: Open Portuguese LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: FaQuAD NLI
          type: ruanchaves/faquad-nli
          split: test
          args:
            num_few_shot: 15
        metrics:
          - type: f1_macro
            value: 57.79
            name: f1-macro
        source:
          url: >-
            https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b
          name: Open Portuguese LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: HateBR Binary
          type: ruanchaves/hatebr
          split: test
          args:
            num_few_shot: 25
        metrics:
          - type: f1_macro
            value: 77.24
            name: f1-macro
        source:
          url: >-
            https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b
          name: Open Portuguese LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: PT Hate Speech Binary
          type: hate_speech_portuguese
          split: test
          args:
            num_few_shot: 25
        metrics:
          - type: f1_macro
            value: 68.5
            name: f1-macro
        source:
          url: >-
            https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b
          name: Open Portuguese LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: tweetSentBR
          type: eduagarcia-temp/tweetsentbr
          split: test
          args:
            num_few_shot: 25
        metrics:
          - type: f1_macro
            value: 63
            name: f1-macro
        source:
          url: >-
            https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b
          name: Open Portuguese LLM Leaderboard

noxinc/Mistral-portuguese-luana-7b-Q5_K_M-GGUF

This model was converted to GGUF format from rhaymison/Mistral-portuguese-luana-7b 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 noxinc/Mistral-portuguese-luana-7b-Q5_K_M-GGUF --model mistral-portuguese-luana-7b.Q5_K_M.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo noxinc/Mistral-portuguese-luana-7b-Q5_K_M-GGUF --model mistral-portuguese-luana-7b.Q5_K_M.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 mistral-portuguese-luana-7b.Q5_K_M.gguf -n 128