Instructions to use Quatfit/Riko-1.1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Quatfit/Riko-1.1B with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Quatfit/Riko-1.1B",
	filename="Premium-Model.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Quatfit/Riko-1.1B with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Quatfit/Riko-1.1B
# Run inference directly in the terminal:
llama-cli -hf Quatfit/Riko-1.1B

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Quatfit/Riko-1.1B
# Run inference directly in the terminal:
llama-cli -hf Quatfit/Riko-1.1B

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 Quatfit/Riko-1.1B
# Run inference directly in the terminal:
./llama-cli -hf Quatfit/Riko-1.1B

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 Quatfit/Riko-1.1B
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Quatfit/Riko-1.1B

Use Docker

docker model run hf.co/Quatfit/Riko-1.1B

LM Studio
Jan

vLLM

How to use Quatfit/Riko-1.1B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Quatfit/Riko-1.1B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Quatfit/Riko-1.1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Quatfit/Riko-1.1B

Ollama
How to use Quatfit/Riko-1.1B with Ollama:
```
ollama run hf.co/Quatfit/Riko-1.1B
```

Unsloth Studio new

How to use Quatfit/Riko-1.1B 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 Quatfit/Riko-1.1B 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 Quatfit/Riko-1.1B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Quatfit/Riko-1.1B to start chatting

Pi new

How to use Quatfit/Riko-1.1B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Quatfit/Riko-1.1B

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": "Quatfit/Riko-1.1B"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Quatfit/Riko-1.1B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Quatfit/Riko-1.1B

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 Quatfit/Riko-1.1B

Run Hermes

hermes

Docker Model Runner
How to use Quatfit/Riko-1.1B with Docker Model Runner:
```
docker model run hf.co/Quatfit/Riko-1.1B
```

Lemonade

How to use Quatfit/Riko-1.1B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Quatfit/Riko-1.1B

Run and chat with the model

lemonade run user.Riko-1.1B-{{QUANT_TAG}}

List all available models

lemonade list

Riko-1.1B

Model: Quatfit/Riko-1.1B
Format: GGUF (single-file GGUF binary, ready for llama.cpp and compatible runtimes)
Approx. size: 1.1B parameters (GGUF quantized)
License: CC-BY-NC-2.0 (Creative Commons Attribution-NonCommercial 2.0)
Last updated: 2026-05-04

Model summary

Riko-1.1B is a 1.1 billion-parameter causal language model packaged as a GGUF file for lightweight, local inference. It is intended for research, experimentation, and non-commercial projects where a compact, efficient model is needed for on-device or offline usage.

Strengths

Compact and optimized for low-latency local inference.
Packaged as GGUF for direct compatibility with llama.cpp and other GGUF-supporting runtimes.
Good for prototyping conversational agents, creative generation, and small-scale research tasks.

Limitations

May produce incorrect or biased outputs; not suitable for safety-critical or high-stakes tasks without human oversight.
Non-commercial license restricts use in paid or commercial applications.

License

This model is released under Creative Commons Attribution-NonCommercial 2.0 (CC-BY-NC-2.0).

Key points:

Attribution: You must give appropriate credit (model name, repo, and license).
Non-Commercial: You may not use the model for commercial purposes.

Include an attribution notice when distributing outputs derived from the model.

Files in this repository

README.md — this file.
Premium-Model.gguf — primary model file (GGUF).

If the GGUF embeds tokenizer and metadata, a separate tokenizer/ folder may not be needed. Verify with your conversion/export step.

Quick local usage (llama.cpp)

Requirements: a build of llama.cpp or another GGUF-compatible runtime.

Example commands:

# basic interactive
./main -m Premium-Model.gguf

# single prompt generation (non-interactive)
./main -m Premium-Model.gguf -p "Hi Baby" -n 128

# recommended example with sampling params
./main -m Premium-Model.gguf -p "Hi Baby!" -n 128 -c 2048 -b 256 --temp 0.9 --repeat_penalty 1.1

Downloads last month: 15

GGUF

Model size

1B params

Architecture

llama

Hardware compatibility

We're not able to determine the quantization variants.

View all variants