Instructions to use ecastera/gemma-2-2b-it-q2_k.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 ecastera/gemma-2-2b-it-q2_k.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 ecastera/gemma-2-2b-it-q2_k.gguf:Q2_K
# Run inference directly in the terminal:
llama cli -hf ecastera/gemma-2-2b-it-q2_k.gguf:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ecastera/gemma-2-2b-it-q2_k.gguf:Q2_K
# Run inference directly in the terminal:
llama cli -hf ecastera/gemma-2-2b-it-q2_k.gguf:Q2_K

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 ecastera/gemma-2-2b-it-q2_k.gguf:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf ecastera/gemma-2-2b-it-q2_k.gguf:Q2_K

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 ecastera/gemma-2-2b-it-q2_k.gguf:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ecastera/gemma-2-2b-it-q2_k.gguf:Q2_K

Use Docker

docker model run hf.co/ecastera/gemma-2-2b-it-q2_k.gguf:Q2_K

LM Studio
Jan
Ollama
How to use ecastera/gemma-2-2b-it-q2_k.gguf with Ollama:
```
ollama run hf.co/ecastera/gemma-2-2b-it-q2_k.gguf:Q2_K
```

Unsloth Studio

How to use ecastera/gemma-2-2b-it-q2_k.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 ecastera/gemma-2-2b-it-q2_k.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 ecastera/gemma-2-2b-it-q2_k.gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ecastera/gemma-2-2b-it-q2_k.gguf to start chatting

Docker Model Runner
How to use ecastera/gemma-2-2b-it-q2_k.gguf with Docker Model Runner:
```
docker model run hf.co/ecastera/gemma-2-2b-it-q2_k.gguf:Q2_K
```

Lemonade

How to use ecastera/gemma-2-2b-it-q2_k.gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ecastera/gemma-2-2b-it-q2_k.gguf:Q2_K

Run and chat with the model

lemonade run user.gemma-2-2b-it-q2_k.gguf-Q2_K

List all available models

lemonade list

Atomic Chat

Quantization of gemma-2-9b-it-abliterated for edge devices 4.9Gb footprint
Original model: https://huggingface.co/IlyaGusev/gemma-2-9b-it-abliterated
All quants made using imatrix option with dataset from here
Using llama.cpp compiled with CUDA support for quantization and inference:

ggml_cuda_init: found 2 CUDA devices: Device 0: NVIDIA GeForce RTX 4060 Ti, compute capability 8.9, VMM: yes Device 1: NVIDIA GeForce RTX 3060, compute capability 8.6, VMM: yes version: 3982 (cc2983d3) built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu

Downloads last month: 10

GGUF

Model size

3B params

Architecture

gemma2

Hardware compatibility

2-bit

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