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

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Brunobkr/OFFELLIA_Quantis 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 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 serve -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 Desktop

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 serve -hf Brunobkr/OFFELLIA_Quantis:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/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

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

Hermes Agent

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 serve -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

Atomic Chat

OpenClaw

How to use Brunobkr/OFFELLIA_Quantis with OpenClaw:

Start the llama.cpp server

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

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "Brunobkr/OFFELLIA_Quantis:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

ΩFFFΣLLIa • llama.cpp • OFFFELLIA_PURE

OFFFELLIA_PURE Web UI -

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  ╚██████╔╝██║     ██║     ███████╗███████╗███████╗██║██║  ██║-PURE
   ╚═════╝ ╚═╝     ╚═╝     ╚══════╝╚══════╝╚══════╝╚═╝╚═╝  ╚═╝

High-Performance LLM / VLM Inference & Autonomous Agentic Ecosystem in Pure C/C++

📖 Visão Geral

ΩFFFΣLLIa • llama.cpp • AlgMor24 é um fork avançado, destravado e de alta performance do ecossistema llama.cpp. Este projeto integra inferência local de última geração em C/C++ com um motor agêntico autônomo multi-turn, suporte nativo a FIM (Fill-in-the-Middle) para geração e preenchimento de código, Speculative Decoding otimizado para programação, integração de ferramentas MCP (Model Context Protocol) e uma interface Web moderna em SvelteKit/Vite com a identidade visual Cyberpunk Neon Fire.

✨

git clone https://github.com/brunoconta1980-tech/llama_OFFFELLIA_1984

cd llama_OFFFELLIA_1984

cmake -B build
-DGGML_VULKAN=ON
-DLLAMA_BUILD_WEBUI=ON
-DLLAMA_SERVER_TOOLS=ON

cmake --build build -j

cmake -S . -B build-vulkan
-DGGML_VULKAN=ON
-DLLAMA_BUILD_WEBUI=ON
-DLLAMA_SERVER_TOOLS=ON

cmake --build build-vulkan -j

Comando sugerido:

"/home/userk21/llama_OFFFELLIA_1984/build/bin/llama-server"
-m "/home/userk21/Área de trabalho/userk21/LLMS/ΩFFFΣLLIα_IQ4_NL_gemma-4-26B-A4B-it.gguf"
-ngl 99 --n-cpu-moe 99
-c 50000
-ctk q8_0
-ctv q8_0
-t 4
-tb 4
-b 2048
-ub 1024
-fa on
--cpu-strict 1
--parallel 1
--agent
--tools all
--reasoning auto
--kv-unified
--load-mode mmap
--cors-origins "*"
--webui-mcp-proxy
--threads-http -1
--port 5173
--host 127.0.0.1

📜 Licença

Distribuído sob a licença MIT. Veja o arquivo LICENSE para mais detalhes. Gracias https://github.com/charlie12345/ROCmFPX

ΩFFFΣLLIa • llama.cpp • AlgMor24 — Inferência Local, Descentralizada e Sem Limites

Downloads last month: 1,686

GGUF

Model size

8B params

Architecture

qwen2vl

Hardware compatibility

4-bit

8-bit

16-bit

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