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おはようございます🩵 昨日は #AGPT# プレイヤーとしてでていました！ レディース12位🥺 ずっと苦しい状況だったからひたすら耐えポーカーだった😂 今日はラストMC！ 会場でお待ちしてます🙋🏻‍♀️💫

◤ AGPT Season 2 MC発表 ◢ Season 2を盛り上げるMCを発表します！ ┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈ 🎙️ 谷口彩菜 @ayana9932 🎙️ うだちー @7udashi28 🎙️ 篠崎彩奈 @ayana18_48 ┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈ 3名の洗練されたトークで、 Season 2を華やかに盛り上げます。 是非会場でお楽しみください🎤 ◈ 開催概要 ◈ 🗓️ 日程：2026.5.28（木）- 5.31（日） 📍 会場：ベルサール飯田橋ファースト ▼ プレイヤーズガイド ▼ 皆様のご参加を心よりお待ち申し上げております。 #AGPT# #直感はデータで進化する#

#AGPT# プレイヤーとしては二つインマネ✌🏻 大家さんとのTAG戦 18位 そしてLadiesは4位でした☺️✨ Ladiesは優勝目指してたから悔しかったけど頑張った！！ めちゃくちゃ楽しかったです！！！♡

#AGPT# 3日間 MCを務めさせていただきました🃏🎤 初めてのMCで拙い部分もあったと思いますが、会場にお越しくださった皆様のおかげで無事に務めることができました！ はぁ〜〜ずっとずっと緊張してたぁあ🫨 ありがとうございました☺︎🩵

📽️ New 4 hour (lol) video lecture on YouTube: "Let’s reproduce GPT-2 (124M)" The video ended up so long because it is... comprehensive: we start with empty file and end up with a GPT-2 (124M) model: - first we build the GPT-2 network - then we optimize it to train very fast - then we set up the training run optimization and hyperparameters by referencing GPT-2 and GPT-3 papers - then we bring up model evaluation, and - then cross our fingers and go to sleep. In the morning we look through the results and enjoy amusing model generations. Our "overnight" run even gets very close to the GPT-3 (124M) model. This video builds on the Zero To Hero series and at times references previous videos. You could also see this video as building my nanoGPT repo, which by the end is about 90% similar. Github. The associated GitHub repo contains the full commit history so you can step through all of the code changes in the video, step by step. Chapters. On a high level Section 1 is building up the network, a lot of this might be review. Section 2 is making the training fast. Section 3 is setting up the run. Section 4 is the results. In more detail: 00:00:00 intro: Let’s reproduce GPT-2 (124M) 00:03:39 exploring the GPT-2 (124M) OpenAI checkpoint 00:13:47 SECTION 1: implementing the GPT-2 nn.Module 00:28:08 loading the huggingface/GPT-2 parameters 00:31:00 implementing the forward pass to get logits 00:33:31 sampling init, prefix tokens, tokenization 00:37:02 sampling loop 00:41:47 sample, auto-detect the device 00:45:50 let’s train: data batches (B,T) → logits (B,T,C) 00:52:53 cross entropy loss 00:56:42 optimization loop: overfit a single batch 01:02:00 data loader lite 01:06:14 parameter sharing wte and lm_head 01:13:47 model initialization: std 0.02, residual init 01:22:18 SECTION 2: Let’s make it fast. GPUs, mixed precision, 1000ms 01:28:14 Tensor Cores, timing the code, TF32 precision, 333ms 01:39:38 float16, gradient scalers, bfloat16, 300ms 01:48:15 torch.compile, Python overhead, kernel fusion, 130ms 02:00:18 flash attention, 96ms 02:06:54 nice/ugly numbers. vocab size 50257 → 50304, 93ms 02:14:55 SECTION 3: hyperpamaters, AdamW, gradient clipping 02:21:06 learning rate scheduler: warmup + cosine decay 02:26:21 batch size schedule, weight decay, FusedAdamW, 90ms 02:34:09 gradient accumulation 02:46:52 distributed data parallel (DDP) 03:10:21 datasets used in GPT-2, GPT-3, FineWeb (EDU) 03:23:10 validation data split, validation loss, sampling revive 03:28:23 evaluation: HellaSwag, starting the run 03:43:05 SECTION 4: results in the morning! GPT-2, GPT-3 repro 03:56:21 shoutout to llm.c, equivalent but faster code in raw C/CUDA 03:59:39 summary, phew, build-nanogpt github repo

One of my favorite results in 2022 was that it's not enough to just think step by step. You must also make sure to get the right answer :D (actually a nice insight into a psychology of a GPT; it pays to condition on a high reward)

Nice new paper improving image generation and (generative) unsupervised representation learning uses ViT instead of CNN to improve VQGAN into a new "ViT-VQGAN" image patch tokenizer. Tokens are then fed into a GPT for image generation, or linear probing.