metadata
license: gemma
tags:
- gemma3
- gemma
- google
- functiongemma
- mlx
- mlx
- mlx-my-repo
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: mlx-community/functiongemma-270m-it-bf16
introvoyz041/functiongemma-270m-it-bf16-mlx-4Bit
The Model introvoyz041/functiongemma-270m-it-bf16-mlx-4Bit was converted to MLX format from mlx-community/functiongemma-270m-it-bf16 using mlx-lm version 0.28.3.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("introvoyz041/functiongemma-270m-it-bf16-mlx-4Bit")
prompt="hello"
if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)