Instructions to use AcademiaSD/MiniMax-H3-NF4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use AcademiaSD/MiniMax-H3-NF4 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("AcademiaSD/MiniMax-H3-NF4", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
File size: 831 Bytes
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license: other
license_name: minimax-h3-community-license-agreement
license_link: https://huggingface.co/MiniMaxAI/MiniMax-H3/blob/main/LICENSE
base_model: MiniMaxAI/MiniMax-H3
base_model_relation: quantized
tags: [nf4, lora-training]
---
# MiniMax-H3 — NF4 for LoRA training
NF4 (4-bit) version of [MiniMax-H3](https://huggingface.co/MiniMaxAI/MiniMax-H3) (transformer, Qwen3-VL-32B text encoder and VAEs, 41 GB instead of 498 GB), used by [AcademiaSD LoRAlab Trainer Studio](https://github.com/AcademiaSD/AcademiaSD_LoRAlab-TrainerStudio) to train MiniMax-H3 LoRAs and RefMods on consumer GPUs. The trainer downloads it automatically.
These weights are a derivative of MiniMax-H3 and are distributed under the same **MiniMax-H3 Community License Agreement**. Read it before using the model or the LoRAs trained with it.
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