Viewing enriched Twitter post
@joburgai @_xpn_ This is for illustration purposes, so I am only focused on math tasks. E.g., consider the MATH dataset with 12,500 math problems. If the 12,000 samples that are not in MATH-500 (which is the test set), distill answers from the largest Qwen3 model, you can improve the accuracy of the 0.6B model from 15.3% to 45.8% on MATH-500, which is an amazing jump.
{
"score": 0.4,
"score_components": {
"author": 0.09,
"engagement": 0.0,
"quality": 0.1,
"source": 0.135,
"nlp": 0.05,
"recency": 0.025
},
"scored_at": "2026-03-08T20:21:23.646132",
"import_source": "api_import",
"source_tagged_at": "2026-03-08T20:21:23.646141",
"enriched": true,
"enriched_at": "2026-03-08T20:21:23.646143"
}{
"type": "tweet",
"id": "2030740358221668738",
"url": "https://x.com/rasbt/status/2030740358221668738",
"twitterUrl": "https://twitter.com/rasbt/status/2030740358221668738",
"text": "@joburgai @_xpn_ This is for illustration purposes, so I am only focused on math tasks. E.g., consider the MATH dataset with 12,500 math problems. If the 12,000 samples that are not in MATH-500 (which is the test set), distill answers from the largest Qwen3 model, you can improve the accuracy of the 0.6B model from 15.3% to 45.8% on MATH-500, which is an amazing jump.",
"source": "Twitter for iPhone",
"retweetCount": 0,
"replyCount": 0,
"likeCount": 0,
"quoteCount": 0,
"viewCount": 3,
"createdAt": "Sun Mar 08 20:20:05 +0000 2026",
"lang": "en",
"bookmarkCount": 0,
"isReply": true,
"inReplyToId": "2030738982666166681",
"conversationId": "2030637125930975580",
"displayTextRange": [
17,
296
],
"inReplyToUserId": "1736198323667599360",
"inReplyToUsername": "joburgai",
"author": {
"type": "user",
"userName": "rasbt",
"url": "https://x.com/rasbt",
"twitterUrl": "https://twitter.com/rasbt",
"id": "865622395",
"name": "Sebastian Raschka",
"isVerified": false,
"isBlueVerified": true,
"verifiedType": null,
"profilePicture": "https://pbs.twimg.com/profile_images/1661187442043486209/a3E4t1eV_normal.jpg",
"coverPicture": "https://pbs.twimg.com/profile_banners/865622395/1742309979",
"description": "",
"location": "United States",
"followers": 404686,
"following": 1134,
"status": "",
"canDm": false,
"canMediaTag": true,
"createdAt": "Sun Oct 07 02:06:16 +0000 2012",
"entities": {
"description": {
"urls": []
},
"url": {}
},
"fastFollowersCount": 0,
"favouritesCount": 24211,
"hasCustomTimelines": true,
"isTranslator": false,
"mediaCount": 2078,
"statusesCount": 19470,
"withheldInCountries": [],
"affiliatesHighlightedLabel": {},
"possiblySensitive": false,
"pinnedTweetIds": [
"1999847254367117736"
],
"profile_bio": {
"description": "ML/AI research engineer. Ex stats professor.\nAuthor of \"Build a Large Language Model From Scratch\" (https://t.co/O8LAAMRzzW) & reasoning (https://t.co/5TueQKx2Fk)",
"entities": {
"description": {
"hashtags": [],
"symbols": [],
"urls": [
{
"display_url": "amzn.to/4fqvn0D",
"expanded_url": "https://amzn.to/4fqvn0D",
"indices": [
100,
123
],
"url": "https://t.co/O8LAAMRzzW"
},
{
"display_url": "mng.bz/lZ5B",
"expanded_url": "https://mng.bz/lZ5B",
"indices": [
138,
161
],
"url": "https://t.co/5TueQKx2Fk"
}
],
"user_mentions": []
},
"url": {
"urls": [
{
"display_url": "sebastianraschka.com",
"expanded_url": "https://sebastianraschka.com",
"indices": [
0,
23
],
"url": "https://t.co/HrtQQ5tgJl"
}
]
}
}
},
"isAutomated": false,
"automatedBy": null
},
"extendedEntities": {},
"card": null,
"place": {},
"entities": {
"hashtags": [],
"symbols": [],
"urls": [],
"user_mentions": []
},
"quoted_tweet": null,
"retweeted_tweet": null,
"isLimitedReply": false,
"article": null
}