metadata
license: gemma
library_name: mlx
pipeline_tag: text-generation
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extra_gated_prompt: >-
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tags:
- automatic-speech-recognition
- automatic-speech-translation
- audio-text-to-text
- video-text-to-text
- mlx
base_model: google/gemma-3n-E4B
huseyincavus/gemma-3n-E4B-it-4bit-mlx
This model huseyincavus/gemma-3n-E4B-it-4bit-mlx was converted to MLX format from google/gemma-3n-E4B using mlx-lm version 0.26.2.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("huseyincavus/gemma-3n-E4B-it-4bit-mlx")
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)