Instructions to use safwanahmadkhan/gemma-4-12B-it-DFlash-8bit-mlx-fp16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use safwanahmadkhan/gemma-4-12B-it-DFlash-8bit-mlx-fp16 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("safwanahmadkhan/gemma-4-12B-it-DFlash-8bit-mlx-fp16") 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 safwanahmadkhan/gemma-4-12B-it-DFlash-8bit-mlx-fp16 with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "safwanahmadkhan/gemma-4-12B-it-DFlash-8bit-mlx-fp16" --prompt "Once upon a time"
- Atomic Chat
- Downloads last month
- 96
Model size
0.7B params
Tensor type
F16
·
Hardware compatibility
Log In to add your hardware
Quantized
Model tree for safwanahmadkhan/gemma-4-12B-it-DFlash-8bit-mlx-fp16
Base model
z-lab/gemma4-12B-it-DFlash