sayakpaul
/ inference_with_torchao_serialized.py
Last active
August 29, 2025 11:42
Shows how to run Flux schnell under 17GBs without bells and whistles. It additionally shows how to serialize the quantized checkpoint and load it back.
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| import torch | |
| from huggingface_hub import hf_hub_download | |
| from diffusers import FluxTransformer2DModel, DiffusionPipeline | |
| dtype, device = torch.bfloat16, "cuda" | |
| ckpt_id = "black-forest-labs/FLUX.1-schnell" | |
| with torch.device("meta"): | |
| config = FluxTransformer2DModel.load_config(ckpt_id, subfolder="transformer") | |
| model = FluxTransformer2DModel.from_config(config).to(dtype) |
karpathy
/ min-char-rnn.py
Last active
October 23, 2025 16:55
Minimal character-level language model with a Vanilla Recurrent Neural Network, in Python/numpy
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| """ | |
| Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy) | |
| BSD License | |
| """ | |
| import numpy as np | |
| # data I/O | |
| data = open('input.txt', 'r').read() # should be simple plain text file | |
| chars = list(set(data)) | |
| data_size, vocab_size = len(data), len(chars) |