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# Example usage:
# python merge_peft.py --base_model=meta-llama/Llama-2-7b-hf --peft_model=./qlora-out --hub_id=alpaca-qlora

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

import argparse

def get_args():

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from graphviz import Digraph
import torch
from torch.autograd import Variable, Function

def iter_graph(root, callback):
    queue = [root]
    seen = set()
    while queue:
        fn = queue.pop()
        if fn in seen:

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pip install streamlit
pip install spacy
python -m spacy download en_core_web_sm
python -m spacy download en_core_web_md
python -m spacy download de_core_news_sm

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05/05/2018

2018: Speech2Vec: A Sequence-to-Sequence Framework for Learning Word Embeddings from Speech

Projects audio files that contains one word of speech into a hyper-dimension space just like Word2Vec. Uses "Force Aligment" to split audio into words (which requires text). Pad the audio segments with zeros, do MFCC, feed into encoder-decoder which uses RMSE. They also add noise to the signal and make the network denoise it. LibriSpeech 500 hour of audio. Not sure how it can incorporated in an ASR or TTS systems. The audio file has to be paired with a text otherwise Speech2Vec cannot split the audio file into words using "Forced Alignment" method. It is used to query if the spoken word is similar to an existing word in the corpus.

2016: Neural Machine Translation of Rare Words with Subword Units (BPE)

BPE data compression tool that combines most frequent pair of bytes with one. It works well with Named Entity, loadwords and morphologically complex words. Handles OOVs well and rare words. You can