Instructions to use mlx-community/gemma-2b-coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/gemma-2b-coder with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # if on a CUDA device, also pip install mlx[cuda] # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("mlx-community/gemma-2b-coder") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use mlx-community/gemma-2b-coder with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "mlx-community/gemma-2b-coder" --prompt "Once upon a time"
- Atomic Chat
- Xet hash:
- 00392e983ad85f6a9b2f104cf88e733e24bc4ec2af9004a576a4b8f89ff22a60
- Size of remote file:
- 2.16 GB
- SHA256:
- a66edc132fbac405ae9e6e793a483d810d43417bfd7b96ae40400112e0f583d8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.