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bill_depth bill_length wing_length location mass sex ID
14.3 48.2 210 loc_2 4600 0 284
14.4 48.4 203 loc_2 4625 0 101
18.4 NA 200 loc_3 3400 0 400
14.9821138211382 47.5048780487805 NA NA 4800 0 98
18.9821138211382 38.2593070487805 217.186991869919 loc_3 5200 0 103
ID species
2 A
5 C
7 C
8 B
9 C
{
"AFAIK": "As Far As I Know",
"AFK": "Away From Keyboard",
"ASAP": "As Soon As Possible",
"ATK": "At The Keyboard",
"ATM": "At The Moment",
"A3": "Anytime, Anywhere, Anyplace",
"BAK": "Back At Keyboard",
"BBL": "Be Back Later",
"BBS": "Be Back Soon",
We can make this file beautiful and searchable if this error is corrected: It looks like row 8 should actually have 9 columns, instead of 3 in line 7.
CUSTOMER_NAME,PLANT_NAME,LATITUDE,LONGITUDE,ELEVATION,FUEL_N2_MOL_PCT,FUEL_MW,FUEL_LHV,CO2_FUEL_RATIO
SPIFFY,SPIRITUAL-POLECAT,61.170355655416756,42.87476722769898,112.0,4.44506304774041,16.572225178958153,21514.22236545268,2.6218034758534565
NONCHALANT,NIFTY-ROOK,37.55451549722366,49.90821662683808,-29.0,1.0531449774702362,16.16609726073242,21526.470829320926,2.714869756572029
NONCHALANT,PREHISTORIC-PETREL,29.190865869410757,60.49170182347541,1552.426025390625,10.298848060446083,17.273122273669447,21494.438671718366,2.461189337194369
NONCHALANT,THERAPEUTIC-LIONFISH,13.253365033156228,76.41105642809447,867.591552734375,13.188813728960108,17.61914942302331,21485.25197916242,2.3818954537334576
SOFT,ABORIGINAL-PICULET,-68.63200204949257,66.15530078737422,1253.15283203125,7.581915824443374,16.947813109323903,21503.41743616074,2.5357355888817
SOFT,CARMINE-REINDEER,-37.9973686323425,178.14485465045027,518.8723754882812,14.604307504874159,17.7886321547886,21480.88277080532,2.3430576226374615
SOFT,IDEALISTIC-DODO,-31
title date link
compute embeddings for tweets in tweets.json 2024-03-18T08:39:57.724Z https://gist.github.com/gd3kr/c4c0687a5f7e91b1a84bcacea6500011
Eric Zhu "WEBSITE" 2024-03-18T08:39:11.512Z https://ericfzhu.com/?windows=blog%3Binspo%3Bworks
Little guide to building Large Language Models in 2024 - Google Slides 2024-03-18T07:19:41.286Z https://docs.google.com/presentation/d/1IkzESdOwdmwvPxIELYJi8--K3EZ98_cL6c5ZcLKSyVg/edit
Everything I'll forget about prompting LLMs 2024-03-18T07:12:49.834Z https://olickel.com/everything-i-know-about-prompting-llms
Codex - see similar quotes w embeddings 2024-03-18T06:11:43.723Z https://codex.ericfzhu.com/?id=2310
How to Fine-Tune an LLM Part 1: Preparing a Dataset for Instruction Tuning | alpaca_ft – Weights & Biases 2024-03-18T04:07:24.922Z https://wandb.ai/capecape/alpaca_ft/reports/How-to-fine-tune-an-LLM-Part-1-Preparing-a-Dataset-for-Instruction-Tuning--Vmlldzo1NTcxNzE2
Pix2Struct 2024-03-18T03:20:36.578Z https://huggingface.co/docs/transformers/en/model_doc/pix2s
title date link
compute embeddings for tweets in tweets.json 2024-03-18T08:39:57.724Z https://gist.github.com/gd3kr/c4c0687a5f7e91b1a84bcacea6500011
Eric Zhu "WEBSITE" 2024-03-18T08:39:11.512Z https://ericfzhu.com/?windows=blog%3Binspo%3Bworks
Little guide to building Large Language Models in 2024 - Google Slides 2024-03-18T07:19:41.286Z https://docs.google.com/presentation/d/1IkzESdOwdmwvPxIELYJi8--K3EZ98_cL6c5ZcLKSyVg/edit
Everything I'll forget about prompting LLMs 2024-03-18T07:12:49.834Z https://olickel.com/everything-i-know-about-prompting-llms
Codex - see similar quotes w embeddings 2024-03-18T06:11:43.723Z https://codex.ericfzhu.com/?id=2310
How to Fine-Tune an LLM Part 1: Preparing a Dataset for Instruction Tuning | alpaca_ft – Weights & Biases 2024-03-18T04:07:24.922Z https://wandb.ai/capecape/alpaca_ft/reports/How-to-fine-tune-an-LLM-Part-1-Preparing-a-Dataset-for-Instruction-Tuning--Vmlldzo1NTcxNzE2
Pix2Struct 2024-03-18T03:20:36.578Z https://huggingface.co/docs/transformers/en/model_doc/pix2s
This file has been truncated, but you can view the full file.
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import argparse
import os
from subprocess import run
import yt_dlp
from openai import OpenAI
client = OpenAI(api_key="") # YOUR KEY
# NV-Embed-v2 Medical Text Embedding and Visualization
# This script demonstrates how to use NVIDIA's NV-Embed-v2 model for medical text embeddings
# and visualize the results using t-SNE
import numpy as np
import torch
import torch.nn.functional as F
from torch.nn import DataParallel
from tqdm import tqdm
from transformers import AutoModel