NTT123

import math

import torch
from torch.optim import AdamW


class MemoryEfficientAdamW(AdamW):
    """
    Memory Efficient AdamW optimizer that keeps parameters and gradients on GPU
    but optimizer states on CPU when enabled.

NTT123

"""
This script converts a Hugging Face LLaMA3 model checkpoint to the original LLaMA3 checkpoint format.

Usage example:
python convert_hf_to_llama3.py --hf_model_path "path/to/hf/model" --output_path "path/to/output"
"""

import torch
from transformers import LlamaForCausalLM
import os

NTT123

mutiann

'''
Implementation for https://arxiv.org/abs/1906.00672

Tips: The code could be directly used in place of BadahnauMonotonicAttention in Tensorflow codes. Similar to its 
base class in the Tensorflow seq2seq codebase,  you may use "hard" for hard inference, or "parallel" for training or 
soft inference. "recurrent" mode in BadahnauMonotonicAttention is not supported. 

If you have already trained another model using BadahnauMonotonicAttention, the model could be reused, otherwise you 
possibly have to tune the score_bias_init, which, similar to that in Raffel et al., 2017, is determined a priori to 
suit the moving speed of the alignments, i.e. speed of speech of your training corpus in TTS cases. So 

knowsuchagency

"""
Add mypy type-checking cell magic to jupyter/ipython.

Save this script to your ipython profile's startup directory.

IPython's directories can be found via `ipython locate [profile]` to find the current ipython directory and ipython profile directory, respectively.

For example, this file could exist on a path like this on mac:

/Users/yourusername/.ipython/profile_default/startup/typecheck.py