-
simple example.
import torch dtype =
torch.float
device =
torch.device("cpu") #
Execute all
calculations on the CPU #
device =
torch.device("cuda:0") # Executes...
- from_pretrained(f"google-t5/{name}")
torch_
dtype =
torch.float16
model = AutoModelForSeq2SeqLM.from_config(config,
torch_
dtype=
torch_
dtype) total, enc, dec = count_parameters(model)...
- 64-bit integer. header: JSON UTF-8 string,
formatted as {"TENSOR_NAME": {“
dtype”: “F16”, “shape”: [1, 16, 256], “data_offsets”: [BEGIN, END]}, "NEXT_TENSOR_NAME":...
-
across half, single, and
double floating-point
precisions (as
TensorFlow dtypes: tf.bfloat16 (truncated
floating point), tf.float16, tf.float32, tf.float64)...
