Integrating RAGChecker metrics in Inspect-ai tasks linkedin.com/pulse/integrati… via @LinkedIn

Context window is one of key LLM features, but what vendors declare almost never the same as effective context length. Check out this graph showing @AI21's #Jamba's declared vs. effective #contextwindow

Hack by popular demand! Open-Source Agent #Hackathon with @AndrewYNg & @DeepLearningAI at @agihouse_org Saturday, *August 24th* Stellar schedule planned w/ secret guest speakers, alongside co-hosts, @GroqInc & @langchain. Reserve your spot now --> eu1.hubs.ly/H0bJRt70

📽️ New 4 hour (lol) video lecture on YouTube: "Let’s reproduce GPT-2 (124M)" youtu.be/l8pRSuU81PU 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. github.com/karpathy/build-na… 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

Git remove local branches after they were deleted in remote repo likely by closing PRs : > git fetch --all -p; git branch -vv | grep ": gone]" | awk '{ print $1 }' | xargs -n 1 git branch -d

Prompt is All You Need

Artificial Intelligence: The Good, The Bad and The Ugly - Free public lecture on May 24 by Professor Abu-Mostafa of @Caltech to explain the science of AI in plain language and assess extreme scenarios. work.caltech.edu/watson #MachineLearning #ArtificialIntelligence #AI #chatGPT

is anything missing?

This is his last weekend with us

ChatGPT as an anti-pattern

This applies to job seekers as much as to investors and founders