Instructions to use Brunobkr/OFFELLIA_Quantis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Brunobkr/OFFELLIA_Quantis with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Brunobkr/OFFELLIA_Quantis",
	filename="OFFELLIA_GELab-Engine-7B_IQ4_XS.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Brunobkr/OFFELLIA_Quantis with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Brunobkr/OFFELLIA_Quantis:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Brunobkr/OFFELLIA_Quantis:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Brunobkr/OFFELLIA_Quantis:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Brunobkr/OFFELLIA_Quantis:Q4_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 Brunobkr/OFFELLIA_Quantis:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Brunobkr/OFFELLIA_Quantis:Q4_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 Brunobkr/OFFELLIA_Quantis:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Brunobkr/OFFELLIA_Quantis:Q4_K_M

Use Docker

docker model run hf.co/Brunobkr/OFFELLIA_Quantis:Q4_K_M

LM Studio
Jan
Ollama
How to use Brunobkr/OFFELLIA_Quantis with Ollama:
```
ollama run hf.co/Brunobkr/OFFELLIA_Quantis:Q4_K_M
```

Unsloth Studio new

How to use Brunobkr/OFFELLIA_Quantis 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 Brunobkr/OFFELLIA_Quantis 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 Brunobkr/OFFELLIA_Quantis to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Brunobkr/OFFELLIA_Quantis to start chatting

Pi new

How to use Brunobkr/OFFELLIA_Quantis with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Brunobkr/OFFELLIA_Quantis:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Brunobkr/OFFELLIA_Quantis:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Brunobkr/OFFELLIA_Quantis with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Brunobkr/OFFELLIA_Quantis:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Brunobkr/OFFELLIA_Quantis:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use Brunobkr/OFFELLIA_Quantis with Docker Model Runner:
```
docker model run hf.co/Brunobkr/OFFELLIA_Quantis:Q4_K_M
```

Lemonade

How to use Brunobkr/OFFELLIA_Quantis with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Brunobkr/OFFELLIA_Quantis:Q4_K_M

Run and chat with the model

lemonade run user.OFFELLIA_Quantis-Q4_K_M

List all available models

lemonade list

Brunobkr commited on Feb 25

Commit

cb83331

verified ·

1 Parent(s): aacc3fa

Update README.md

Browse files

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README.md +5 -2

README.md CHANGED Viewed

@@ -23,8 +23,11 @@ base_model:
 - Brunobkr/OFFELLIA_Quantis
 ---
-24/02/2026 ---- NOVIDADES EM BREVE EM RELAÇÃO QUANTIZAÇÃO, ESTOU REFINANDO E FAZENDO TESTES REAIS, EM BREVE POSTO NOVIDADES,
-POR ENQUANTO ESTES MODELOS  AO MENOS SÃO ESTAVEIS. NÃO JUSTIFICA APAGAR ELES, MAS EM BREVE NOVIDADES.
 迫於無奈，我們只好發揮創意。

 - Brunobkr/OFFELLIA_Quantis
 ---
+24/02/2026 ---- O arquivo quants.py ZetaHelicoidal neste repositório é matemáticamente válido dentro de minhas teorias conforme README.md anexo,
+porem o mesmo precisava de modificações funcionais, as quais consegui. As versões futuras de GGUfs após esta data serão de fato inovadoras, e
+sujeitas a análises e avaliações amplamente por todos, os arquivos são livres e opensource. Porêm, postarei apenas os GGUFs gerados pelo
+Zethahelicoidal funcional, os arquivos .py llama.cpp funcionais ainda não serão publicados, estão comigo, e deles serão gerados os pŕóximos
+GGUFs aqui publicados após esta data, qualquer coisa entrem em contato, e deem seu like se gostarem. Obrigado.
 迫於無奈，我們只好發揮創意。