metadata
license: gemma
tags:
- gemma3
- gemma
- google
- functiongemma
- mlx
pipeline_tag: text-generation
library_name: mlx
extra_gated_heading: Access Gemma on Hugging Face
extra_gated_prompt: >-
To access FunctionGemma on Hugging Face, you’re required to review and agree
to Google’s usage license. To do this, please ensure you’re logged in to
Hugging Face and click below. Requests are processed immediately.
extra_gated_button_content: Acknowledge license
base_model: google/functiongemma-270m-it
mlx-community/functiongemma-270m-it-4bit
This model mlx-community/functiongemma-270m-it-4bit was converted to MLX format from google/functiongemma-270m-it using mlx-lm version 0.28.4.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/functiongemma-270m-it-4bit")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)