Instructions to use bond005/meno-tiny-0.1-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bond005/meno-tiny-0.1-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="bond005/meno-tiny-0.1-gguf",
	filename="meno-tiny-0.1-fp16.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 bond005/meno-tiny-0.1-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 bond005/meno-tiny-0.1-gguf
# Run inference directly in the terminal:
llama cli -hf bond005/meno-tiny-0.1-gguf

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf bond005/meno-tiny-0.1-gguf
# Run inference directly in the terminal:
llama cli -hf bond005/meno-tiny-0.1-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 bond005/meno-tiny-0.1-gguf
# Run inference directly in the terminal:
./llama-cli -hf bond005/meno-tiny-0.1-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 bond005/meno-tiny-0.1-gguf
# Run inference directly in the terminal:
./build/bin/llama-cli -hf bond005/meno-tiny-0.1-gguf

Use Docker

docker model run hf.co/bond005/meno-tiny-0.1-gguf

LM Studio
Jan

vLLM

How to use bond005/meno-tiny-0.1-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bond005/meno-tiny-0.1-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": "bond005/meno-tiny-0.1-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/bond005/meno-tiny-0.1-gguf

Ollama
How to use bond005/meno-tiny-0.1-gguf with Ollama:
```
ollama run hf.co/bond005/meno-tiny-0.1-gguf
```

Unsloth Studio

How to use bond005/meno-tiny-0.1-gguf 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 bond005/meno-tiny-0.1-gguf 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 bond005/meno-tiny-0.1-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for bond005/meno-tiny-0.1-gguf to start chatting

How to use bond005/meno-tiny-0.1-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf bond005/meno-tiny-0.1-gguf

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": "bond005/meno-tiny-0.1-gguf"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use bond005/meno-tiny-0.1-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 bond005/meno-tiny-0.1-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 bond005/meno-tiny-0.1-gguf

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use bond005/meno-tiny-0.1-gguf with Docker Model Runner:
```
docker model run hf.co/bond005/meno-tiny-0.1-gguf
```

Lemonade

How to use bond005/meno-tiny-0.1-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull bond005/meno-tiny-0.1-gguf

Run and chat with the model

lemonade run user.meno-tiny-0.1-gguf-{{QUANT_TAG}}

List all available models

lemonade list

Meno-Tiny-0.1-GGUF

Introduction

Meno-Tiny-0.1 is a descendant of the Qwen2.5-1.5B-Instruct model, which was fine-tuned on a special Russian instruct dataset. It is a 1.5B parameter language model with a decoder. It is based on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, etc. The name "Meno" is associated with the adaptation of this model for answering questions from text in the RAG pipeline (in honor of the theory of knowledge as recollection from the Socratic dialogue "Meno").

Quickstart

Check out our llama.cpp documentation for more usage guide.

We advise you to clone llama.cpp and install it following the official guide. We follow the latest version of llama.cpp. In the following demonstration, we assume that you are running commands under the repository llama.cpp.

Since cloning the entire repo may be inefficient, you can manually download the GGUF file that you need or use huggingface-cli:

Install
```
pip install -U huggingface_hub
```

Download:

huggingface-cli download bond005/Meno-Tiny-0.1-GGUF meno-tiny-0.1-fp16.gguf --local-dir . --local-dir-use-symlinks False

For users, to achieve chatbot-like experience, it is recommended to commence in the conversation mode:

./llama-cli -m <gguf-file-path> \
    -co -cnv -p "You are Meno, created by Ivan Bondarenko. You are a helpful assistant." \
    -fa -ngl 80 -n 512

Evaluation & Performance

Detailed evaluation results are reported in this model card.

Citation

If you find our work helpful, feel free to give us a cite.

@misc{bondarenko2024meno,
  title={Meno-Tiny: A Small Russian Language Model for Question Answering and Other Useful NLP Tasks in Russian},
  author={Bondarenko, Ivan},
  publisher={Hugging Face},
  journal={Hugging Face Hub},
  howpublished={\url{https://huggingface.co/bond005/meno-tiny-0.1}},
  year={2024}
}