Instructions to use Ninnix96/Flit-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Ninnix96/Flit-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 Ninnix96/Flit-gguf
# Run inference directly in the terminal:
llama cli -hf Ninnix96/Flit-gguf

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Ninnix96/Flit-gguf
# Run inference directly in the terminal:
llama cli -hf Ninnix96/Flit-gguf

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 Ninnix96/Flit-gguf
# Run inference directly in the terminal:
./llama-cli -hf Ninnix96/Flit-gguf

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 Ninnix96/Flit-gguf
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Ninnix96/Flit-gguf

Use Docker

docker model run hf.co/Ninnix96/Flit-gguf

Llama new
LM Studio
Jan

vLLM

How to use Ninnix96/Flit-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Ninnix96/Flit-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": "Ninnix96/Flit-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Ninnix96/Flit-gguf

Ollama
How to use Ninnix96/Flit-gguf with Ollama:
```
ollama run hf.co/Ninnix96/Flit-gguf
```
Unsloth Desktop

How to use Ninnix96/Flit-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Ninnix96/Flit-gguf

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": "Ninnix96/Flit-gguf"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Ninnix96/Flit-gguf with Docker Model Runner:
```
docker model run hf.co/Ninnix96/Flit-gguf
```

Lemonade

How to use Ninnix96/Flit-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Ninnix96/Flit-gguf

Run and chat with the model

lemonade run user.Flit-gguf-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use Ninnix96/Flit-gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Ninnix96/Flit-gguf

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 Ninnix96/Flit-gguf

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Ninnix96/Flit-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Ninnix96/Flit-gguf

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 "Ninnix96/Flit-gguf" \
  --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"

Flint Preview — IQ2XXS/Q2K/Q8 GGUF

This repository contains flint-IQ2XXS-Q2K-Q8.gguf, a community quantization of RecursiveSI/flint. It is not an official Recursive release.

Download and run

Use a recent llama.cpp build with Flint support:

llama-cli -m flint-IQ2XXS-Q2K-Q8.gguf --temp 1.0 --top-p 0.95 --top-k 20 -p "Who are you?"

Quantization recipe

Tensor class	Quantization
Gate and up experts	IQ2_XXS
Down experts	Q2_K
Other weights	Q8_0

The source model is a 39.8B-parameter mixture-of-experts model with about 2.5B active parameters per token. See the source model card for architecture, recommended generation settings, evaluation, and license details.