@tomgoldstein.bsky.social
Nowadays ML projects feel like they need to be compressed into a few months. Its refreshing to be able to work on something for a few years!
But also a slog.
Thanks @sean-mcleish.bsky.social, Neel Jain, jwkirchenbauer.bsky.social, Siddharth Singh, Brian Bartoldson, Bhavya Kailkhu, Abhinav Bhatele.
Superhero @jonasgeiping.bsky.social started architecture search for this two years ago, & wrote a distributed framework from scratch to handle bugs with AMD 🤯
Huginn is just a proof of concept.
Still, Huggin-3.5B can beat OLMo-7B-0724 (with CoT) at GSM8K by a wide margin (42% vs 29%).
Huginn has half the parameters, 1/3 the training tokens, no explicit fine-tuning, and the LR was never annealed.
Latent reasoning still wins.
Recurrence improves reasoning a lot. To show this, we did a comparison with a standard architecture.
We train a standard 3.5B LLM from scratch on 180B tokens. Then we train a recurrent 3.5B model on the same tokens.
The recurrent model does 5X better on GSM8K.
Huginn was built for reasoning from the ground up, not just fine-tuned on CoT.
We built our reasoning system by putting a recurrent block inside the LLM. On a forward pass, we loop this block a random number of times. By looping it more times, we dial up compute.
New open source reasoning model!
Huginn-3.5B reasons implicitly in latent space 🧠
Unlike O1 and R1, latent reasoning doesn’t need special chain-of-thought training data, and doesn't produce extra CoT tokens at test time.
We trained on 800B tokens 👇
📢PSA: #NeurIPS2024 recordings are now publicly available!
The workshops always have tons of interesting things on at once, so the FOMO is real😵💫 Luckily it's all recorded, so I've been catching up on what I missed.
Thread below with some personal highlights🧵
Here’s what doesn’t check out. They claim $5.5M by assuming a $2 per GPU-hour “rental cost”. On AWS, an H100 (with the same compute cores as an H800) costs $12.28/hour on demand, and $5.39 with a 3-year reservation. H800s are prob worth less, but…$2/hour? Idunno about that.
29.01.2025 17:12 — 👍 3 🔁 0 💬 0 📌 0They'd need to eliminate token routing inefficiency and keep communication volume down (their H800 GPUs communicate slower than the H100s in the benchmark). They accomplish this using pipelined training, which industry labs prefer over the Pytorch FSDP implementation.
29.01.2025 17:12 — 👍 1 🔁 0 💬 1 📌 0
In a Pytorch Foundation Benchmark, the H100 rips through 1500 tokens per second.
For 34B, Pytorch Foundation reports throughput of 1500 tokens/sec per GPU.
With this throughput, DeepSeek’s 2.664M GPU-hour pre-training run would rip through 14.3T tokens. DeepSeek claims to have trained on 14.8T tokens.
This part checks out...but only with killer engineering.
Let’s sanity check DeepSeek’s claim to train on 2048 GPUs for under 2 months, for a cost of $5.6M. It sort of checks out and sort of doesn't.
The v3 model is an MoE with 37B (out of 671B) active parameters. Let's compare to the cost of a 34B dense model. 🧵
