- Libraries
- Transformers
How to use Atharv65/gemma_3_4b with Transformers:
# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Atharv65/gemma_3_4b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Atharv65/gemma_3_4b 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 Atharv65/gemma_3_4b
# Run inference directly in the terminal:
llama cli -hf Atharv65/gemma_3_4b
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Atharv65/gemma_3_4b
# Run inference directly in the terminal:
llama cli -hf Atharv65/gemma_3_4b
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 Atharv65/gemma_3_4b
# Run inference directly in the terminal:
./llama-cli -hf Atharv65/gemma_3_4b
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 Atharv65/gemma_3_4b
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Atharv65/gemma_3_4b
Use Docker
docker model run hf.co/Atharv65/gemma_3_4b
- LM Studio
- Jan
- Ollama
How to use Atharv65/gemma_3_4b with Ollama:
ollama run hf.co/Atharv65/gemma_3_4b
- Unsloth Desktop
- Docker Model Runner
How to use Atharv65/gemma_3_4b with Docker Model Runner:
docker model run hf.co/Atharv65/gemma_3_4b
- Lemonade
How to use Atharv65/gemma_3_4b with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/
lemonade pull Atharv65/gemma_3_4b
Run and chat with the model
lemonade run user.gemma_3_4b-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
