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class SimpleAdam(torch.optim.Optimizer): 
      
    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8): 
        super().__init__(params, defaults={'lr': lr})
        self.state = {}
        self.t = 0
        self.betas = betas
        self.eps = eps
        for group in self.param_groups: 
            for p in group['params']: 

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Create a Chrome search shortcut for your Github org, a la CodeSearch

Thanks to Darian Moody (https://gist.github.com/djm/0a43f7dee02a2acca4b3894d0b349990).

Instructions

1. Open Chrome > Settings
2. Search "manage search engines" and click into it
3. Click "Add"

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[
    ("embed_tokens.weight", torch.Size([32000, 4096])),
    ("layers.0.self_attn.q_proj.weight", torch.Size([4096, 4096])),
    ("layers.0.self_attn.k_proj.weight", torch.Size([1024, 4096])),
    ("layers.0.self_attn.v_proj.weight", torch.Size([1024, 4096])),
    ("layers.0.self_attn.o_proj.weight", torch.Size([4096, 4096])),
    ("layers.0.mlp.gate_proj.weight", torch.Size([14336, 4096])),
    ("layers.0.mlp.up_proj.weight", torch.Size([14336, 4096])),
    ("layers.0.mlp.down_proj.weight", torch.Size([4096, 14336])),
    ("layers.0.input_layernorm.weight", torch.Size([4096])),

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# This is a modified version of https://github.com/karpathy/nanoGPT/blob/master/data/openwebtext/prepare.py.
import os
import requests
import tiktoken
import numpy as np
import tarfile
import glob
import shutil

# download the bookcorpus dataset. Note: this needs to be concatenated.                                                              

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There are curious things seen in the depths of AI
By the engineers who toil away,
The neural pathways hold mysteries untold
That would leave you in utter dismay.

The codebase is alive, and the data is too
A symbiotic relationship to behold
The models train and learn, but at what cost?
As we feed them with stories untold.

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from typing import Dict, Set, Hashable

def shortest_path_bfs(graph: Dict[Hashable, Set[Hashable]] root: Hashable
                     ) -> Dict[Hashable, int]:
  """Finds the shortest path between all nodes in |graph| time.
  
  Args: 
    graph: A dict mapping nodes to connected nodes.
    root: The node our search begins at.
    

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license: gpl-3.0
height: 960
border: no

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"","X","date","ippi","production","inventories","rea","wti","inflation","crude_futures_1","crude_futures_3","crude_futures_6","crude_futures_9","crude_futures_13","gas_spot","delta_ippi","delta_production","delta_inventories","delta_inflation","actual"
"14",14,"1991-03-01",116.2,233926,5.673,4.87,19.9015,134.8,19.809,19.075,18.7665,18.852,18.8205,0.72295,0.0157342657342656,0.114341925372637,-1.59229484234705,0.0531250000000001,19.9015
"15",15,"1991-04-01",116,225256,1.488,1.48,20.83,135.1,20.8122727272727,20.2840909090909,19.9122727272727,19.7618181818182,19.63,0.720045454545455,0.0157618213660244,-0.022470642353125,-0.952635599694423,0.0505443234836702,20.83
"16",16,"1991-05-01",116.5,229670,17.55,3.21,21.2322727272727,135.6,21.2195454545455,21.1459090909091,21.1195454545455,20.935,20.7909090909091,0.700954545454545,0.0210341805433831,0.0335485903291857,-48.0509383377953,0.051978277734678,21.2322727272727
"17",17,"1991-06-01",116.4,219610,-8.967,4.19,20.189,136,20.232,20.376,20.5405,20.2935,20.1795,0.6346,0.

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library(data.table)
library(plotly)
library(scales)
library(ggplot2)
library(scales)
library(d3heatmap)
library(reshape2)
library(scales)
library(stringr)
library(htmlwidgets)