Instructions to use QuantFactory/Gemmasutra-Mini-2B-v1-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 QuantFactory/Gemmasutra-Mini-2B-v1-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 QuantFactory/Gemmasutra-Mini-2B-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Gemmasutra-Mini-2B-v1-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf QuantFactory/Gemmasutra-Mini-2B-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Gemmasutra-Mini-2B-v1-GGUF: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 QuantFactory/Gemmasutra-Mini-2B-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Gemmasutra-Mini-2B-v1-GGUF: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 QuantFactory/Gemmasutra-Mini-2B-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Gemmasutra-Mini-2B-v1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Gemmasutra-Mini-2B-v1-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use QuantFactory/Gemmasutra-Mini-2B-v1-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Gemmasutra-Mini-2B-v1-GGUF:Q4_K_M
```

Unsloth Studio

How to use QuantFactory/Gemmasutra-Mini-2B-v1-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 QuantFactory/Gemmasutra-Mini-2B-v1-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 QuantFactory/Gemmasutra-Mini-2B-v1-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/Gemmasutra-Mini-2B-v1-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use QuantFactory/Gemmasutra-Mini-2B-v1-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Gemmasutra-Mini-2B-v1-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Gemmasutra-Mini-2B-v1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Gemmasutra-Mini-2B-v1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Gemmasutra-Mini-2B-v1-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/Gemmasutra-Mini-2B-v1-GGUF

This is quantized version of TheDrummer/Gemmasutra-Mini-2B-v1 created using llama.cpp

Original Model Card

Join our Discord! https://discord.gg/Nbv9pQ88Xb

Works on Kobold 1.72 and Layla (iOS / Android)

BeaverAI team proudly presents

Gemmasutra Mini 2B v1 🧘

Gemma's been training for ALL of you.

A tiny RP model packing an unbelievable PUNCH. Finetuned by yours truly.

Description

Gone are the days when models below 7B were too small to give you a satisfying RP experience.

Gemmasutra Mini 2B v1 marks the beginning of a new chapter in our local LLM community.

Be it in your browser, your crappy laptop, your mid-tier phone running Layla, or your Raspberry Pi...

This lil 2B will give you a playthrough worth having.

A model for thee, a model for all!

(And yes, it is uncensored and unaligned. Enjoy!)

Usage

For the best experience, use the Gemma Instruct template and modify it to support the system role (e.g., <start_of_turn>system)
Chat Completion works well too
Don't use it for Math