#quantize

[bgh-matt]

A conversation that will absolutely never happen, but I can't stop myself from rehearsing in my head while I heavily edit my drum tracks:

"Hey man your drumming was tight as hell! Did you play to a click?" "No!" "Oh dang, so you're just that dead on?" "Also no!"

[musicproductioninside]

[stonewall-klaxon]

NEW REALEASE — "WILHELM" OUT NOW !

[synthtv]

Why is this so much fun!!? Generative quantized melodies

[pythonprogrammingsnippets]

python reduce an ai model’s size down with quantization and speed it up

Quantizing a model means reducing the number of bits used to represent its weights and activations. This can help make the model smaller and faster, which is good because it means it can run on smaller computers and devices more easily. However, because the model uses fewer bits, it may not be able to represent its parameters as precisely as before. This can lead to some loss of accuracy or quality. So, quantizing a model can be a trade-off between size and speed on one hand, and accuracy or quality on the other. But overall, quantizing is a useful technique that can help make machine learning more accessible and efficient.

import torch import torch.nn as nn from transformers import GPT2Tokenizer, GPT2LMHeadModel # Load the GPT-2 model and tokenizer model_name = 'gpt2' tokenizer = GPT2Tokenizer.from_pretrained(model_name) model = GPT2LMHeadModel.from_pretrained(model_name) # Generate some sample text prompt = "The quick brown fox jumps over the lazy dog." input_ids = tokenizer.encode(prompt, return_tensors='pt') output = model.generate(input_ids, max_length=100, do_sample=True, num_return_sequences=1) sample_text = tokenizer.decode(output[0], skip_special_tokens=True) print("Sample text: ", sample_text) # Quantize the model down quantization_config = { 'activation': { 'dtype': torch.quint8, 'qscheme': torch.per_tensor_affine, 'reduce_range': True }, 'weight': { 'dtype': torch.qint8, 'qscheme': torch.per_tensor_symmetric, 'reduce_range': True } } quantized_model = torch.quantization.quantize_dynamic(model, quantization_config, dtype=torch.qint8) # Save the quantized model torch.save(quantized_model.state_dict(), 'quantized_model.pt') ## ------------------------------- Generate using Quantized Model # Load the quantized model quantized_model = GPT2LMHeadModel.from_pretrained(model_name) quantized_model.load_state_dict(torch.load('quantized_model.pt')) # Generate some sample text using the quantized model output_quantized = quantized_model.generate(input_ids, max_length=100, do_sample=True, num_return_sequences=1) sample_text_quantized = tokenizer.decode(output_quantized[0], skip_special_tokens=True) print("Sample text (quantized): ", sample_text_quantized)

[englishlistwords]

Quantize

transitive verb To limit the possible values of (a magnitude or quantity) to a discrete set of values by quantum mechanical rules.

transitive verb To apply quantum mechanics or the quantum theory to.

v. To limit the number of possible values of a quantity, or states of a system, by applying the rules of quantum mechanics

[radiodeep]

Get your Pre Orders now! Quantize Recordings - DJ Spen presents an incredible EP by Lomax entitled “To The Max”. Release preview today exclusively on Radio Deep! #release #releasepreview #radio #house #quantize #djspen #djlomax #housecafe #radiodeep https://www.instagram.com/p/CIIlVw4AJis/?igshid=1cgmy21k0d0lo

[thedeliblog]

#137 Tudsz japánul számolni?

videó: 35:21 perc

Japán teljesen más világ. Úgy általánosságban. Ezt tudjuk. Nem nagy bölcsesség. De, hogy időről időre tudnak frisset mutatni? A “mostani”, Quantize (Számszerűsítés) című videó egy Kamekyo nevű ifjonc videós 2019-es debütálása, hét, több generációt átfogó deszkással. 35 percen át gyönyörködhetünk a kimért és ötletes trükksorokban, kellemes zenékkel, többnyire ismeretlen spot-okkal és sok-sok, Japánra annyira jellemző éjszakai (értsd: biztonsági őr mentes) deszkázással. A mozivásznon: Junnosuke Yonesaka, Satoru Yoshida, Daisetsu Araki, Raido Tajima, Tenma Fukashiro, Mizuto Watanabe és Naotaka Ohya.