metadata
library_name: mlx
license: other
license_name: nvidia-open-model-license
license_link: >-
https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/
pipeline_tag: text-generation
language:
- en
- es
- fr
- de
- ja
- it
tags:
- nvidia
- pytorch
- mlx
datasets:
- nvidia/Nemotron-Pretraining-Code-v1
- nvidia/Nemotron-CC-v2
- nvidia/Nemotron-Pretraining-SFT-v1
- nvidia/Nemotron-CC-Math-v1
- nvidia/Nemotron-Pretraining-Code-v2
- nvidia/Nemotron-Pretraining-Specialized-v1
- nvidia/Nemotron-CC-v2.1
- nvidia/Nemotron-CC-Code-v1
- nvidia/Nemotron-Pretraining-Dataset-sample
- nvidia/Nemotron-Competitive-Programming-v1
- nvidia/Nemotron-Math-v2
- nvidia/Nemotron-Agentic-v1
- nvidia/Nemotron-Math-Proofs-v1
- nvidia/Nemotron-Instruction-Following-Chat-v1
- nvidia/Nemotron-Science-v1
- nvidia/Nemotron-3-Nano-RL-Training-Blend
track_downloads: true
base_model: nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16
mlx-community/NVIDIA-Nemotron-3-Nano-30B-A3B-MLX-8Bit
This model mlx-community/NVIDIA-Nemotron-3-Nano-30B-A3B-MLX-8Bit was converted to MLX format from nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 using mlx-lm version 0.29.1.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/NVIDIA-Nemotron-3-Nano-30B-A3B-MLX-8Bit")
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)