nightmedia/NVIDIA-Nemotron-Labs-3-Elastic-12B-A2B-mxfp8-mlx
049
NVIDIA-Nemotron-Labs-3-Elastic-12B-A2B-mxfp8-mlx
Brainwaves
arc arc/e boolq hswag obkqa piqa wino
mxfp8 0.433,0.624,0.807,0.593,0.390,0.752,0.602
qx86-hi 0.454,0.658,0.821,0.636,0.412,0.770,0.639
qx64-hi 0.442,0.643,0.824,0.631,0.410,0.765,0.635
mxfp4 0.444,0.634,0.823,0.622,0.390,0.765,0.628
Quant Perplexity Peak Memory Tokens/sec
mxfp8 6.228 ± 0.048 17.04 GB 2165
qx86-hi 5.269 ± 0.039 15.18 GB 2128
qx64-hi 5.407 ± 0.040 12.08 GB 2326
mxfp4 5.786 ± 0.044 10.83 GB 1788This model NVIDIA-Nemotron-Labs-3-Elastic-12B-A2B-mxfp8-mlx was converted to MLX format from DavidAU/NVIDIA-Nemotron-Labs-3-Elastic-12B-A2B using mlx-lm version 0.31.3.
Use with mlx
pip install mlx-lmfrom mlx_lm import load, generate
model, tokenizer = load("NVIDIA-Nemotron-Labs-3-Elastic-12B-A2B-mxfp8-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, return_dict=False,
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)