Text Generation
MLX
English
nemotron
multimodal
mamba2
Mixture of Experts
quantized
rotorquant
kv-cache-modifier
apple-silicon
runtime-modifier
matched-stack
Instructions to use majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-MLX-5bit-RQ-KV with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-MLX-5bit-RQ-KV with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # if on a CUDA device, also pip install mlx[cuda] # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-MLX-5bit-RQ-KV") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
- MLX LM
How to use majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-MLX-5bit-RQ-KV with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-MLX-5bit-RQ-KV" --prompt "Once upon a time"