Gemma 3 QAT
Collection
Quantization Aware Trained (QAT) Gemma 3 checkpoints. The model preserves similar quality as half precision while using 3x less memory. • 29 items • Updated
• 32
Configuration Parsing Warning: In tokenizer_config.json: "tokenizer_config.chat_template" must be one of [string, array]
This model mlx-community/gemma-3-270m-qat-6bit was converted to MLX format from google/gemma-3-270m-qat using mlx-lm version 0.26.3.
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/gemma-3-270m-qat-6bit")
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)
6-bit