youkaichao

import openai
import asyncio

async def get_choice_completion(prompt, choices):
    # Initialize an asynchronous OpenAI client
    async with openai.AsyncClient(base_url="http://127.0.0.1:8000/v1", api_key="abc") as client:
        choice_probs = {}

        # Calculate logprobs for each prompt + choice sequence
        for choice in choices:

allanj

from torch.utils.data import DataLoader
from transformers import AutoTokenizer, PreTrainedTokenizerFast, set_seed, AutoModelForCausalLM, AutoConfig
from tqdm import tqdm
import argparse
import torch
import torch.nn as nn
import logging
from typing import Dict, Tuple
from accelerate import Accelerator, DistributedDataParallelKwargs
from accelerate.logging import get_logger

CharlyWargnier

import streamlit as st

if 'count' not in st.session_state:
    st.session_state.count = 0

if 'quotes' not in st.session_state:
    st.session_state.quotes = [
        "Life is what happens when you're busy making other plans. — John Lennon",
        "Get busy living or get busy dying. — Stephen King",
        "You only live once, but if you do it right, once is enough. — Mae West",

yoavg

Reinforcement Learning for Language Models

Yoav Goldberg, April 2023.

Why RL?

With the release of the ChatGPT model and followup large language models (LLMs), there was a lot of discussion of the importance of "RLHF training", that is, "reinforcement learning from human feedback". I was puzzled for a while as to why RL (Reinforcement Learning) is better than learning from demonstrations (a.k.a supervised learning) for training language models. Shouldn't learning from demonstrations (or, in language model terminology "instruction fine tuning", learning to immitate human written answers) be sufficient? I came up with a theoretical argument that was somewhat convincing. But I came to realize there is an additional argumment which not only supports the case of RL training, but also requires it, in particular for models like ChatGPT. This additional argument is spelled out in (the first half of) a talk by John Schulman from OpenAI. This post pretty much

allanj

Kill process contain certain string

For example, kill command contains python3 -u experiment_main.py

kill $(ps aux | grep '[p]ython3 -u experiment_main.py' | awk '{print $2}')

Hadoop List files by date

hdfs dfs -ls / | sort -k6,7

Mason-McGough

class PointerNetwork(nn.Module):
  """
  From "Pointer Networks" by Vinyals et al. (2017)

  Adapted from pointer-networks-pytorch by ast0414:
    https://github.com/ast0414/pointer-networks-pytorch

  Args:
    n_hidden: The number of features to expect in the inputs.
  """

xuuuluuu

        new_adj = torch.triu(adj, diagonal=1)
        # print(new_adj[-1, :10, :10])
        new_adj1 = torch.bmm(new_adj, new_adj)
        # print(new_adj1[-1, :10, :10])
        new_adj_or = torch.clamp((new_adj + new_adj1), max=1)
        # print('new_adj_or', new_adj_or[-1, :10, :10])
        loop = 1
        while not torch.equal(torch.bmm(new_adj_or, new_adj_or), new_adj1):
            new_adj1 = torch.bmm(new_adj_or, new_adj_or)
            new_adj_or = torch.clamp((new_adj_or + new_adj1), max=1)

ines

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

allanj

"""
IOB1:  O I I B I
IOB2:  O B I B I
"""

from typing import List

def iob2(tags: List[str]):
    """
    Check that tags have a valid IOB format.

allanj

# 
# @author: Allan
# 



def convert(input, output):
    from gensim.models.keyedvectors import KeyedVectors
    embedding = KeyedVectors.load_word2vec_format(input, binary=True)
    f= open(output, 'w', encoding='utf-8')