The Ledger
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Ben Allal: Recent web dumps improve model benchmarks despite synthetic data
“So what we did is we trained different models on these different dumps, and we then computed their performance on popular like NLP benchmarks, and then we computed the aggregated score. And surprisingly, you can see that the latest dumps are actually even bett…”
Sohmers: Positron AI requires zero compilers to run Hugging Face models
“So rather than having like, we don't have a compiler whatsoever. There's no compiler. There's no translator, no tooling that's involved in actually taking those and getting that to, you know, for your common, you know, Huggy Face Transform models to be able to…”
Roucher: AI agents will reach a 90% GAIA score by 2026
“So I think if we solve Gaia, that's like 90% score. That means mostly we double productivity of every task done in front of a computer. And if you take the trend line of the scores so far this should be crossed in 2026 or something.”
Ben Allal: Hugging Face SmolLM2-1.7B outperforms Llama 3.2 models
“So it's a series of three models, which are the best in class in each model size. For example, our 1.7 B model outperforms Lama one B and also .2.”
Patel: Hugging Face libraries achieve only 15% MBU for inference
“Hugging Face's libraries are actually very inefficient, like incredibly inefficient for inference. You get like, 15% MBU on, on, on, on some configurations, like eight, eight, eight, eight, eight, eight, 100, and LLAMA-seventy-beat, you get like, 15%, which is…”
Bakouch: SmolLM 2 scored random on MMLU until 6.5 trillion tokens
“And this is basically until like 6.5 trillion of tokens, which is a lot, to be honest. Until this amount of token, the MMLU in the QA format, meaning that the model have to select which answer is, the model have to output, for example, the right answer is A, o…”
Ben Allal: LLMs can be trained with entirely synthetic pipelines
“Today you can train an LLM with like an entirely synthetic pipeline. For example, you can use our Cosmopedia data sets and you can train a one B model on like a hundred and fifty billion tokens. Those are a hundred percent synthetic, and those are also of good…”
Mlejnsky: Hugging Face uses E2B sandboxes during Open-R1 RL training
“The way HuggingFace, who built the OpenROne project is using us is during like the reinforcement learn, code gen reinforcement learning step where the ROne model the OpenROne model has a training step where they give it a Code problem, and the model needs to g…”
Ben Allal: FineWeb-Edu Outperforms All Other Public Web Datasets
“And as you can see here FineWebEDU outperforms all the other public web datasets by a larger margin on a couple of benchmarks.”
Ethan He: Hugging Face's sequential GEMM loop for Mixtral is inefficient
“Let's also look at the implementation of Mixtro eight by seven on Hagen-Phys transformer. You will soon notice the, in the expert operation there, You would iterate over all of the experts and compute each of the gem operations one by one. We found that this i…”
Writing deployment configs for arbitrary Hugging Face models is now one-shot-able
“One big part of my job a couple years ago was for any arbitrary model that came out on Hugging Face, writing a config for it and kind of getting it up and running, and now the get it up and running config is, is one-shot-able and so, you know, I don't have to …”
Marimo Surpasses 300K Monthly PyPI Downloads and Jupyter's GitHub Stars
“I think last I checked, over 300,000 monthly downloads on PyPy. More GitHub stars than Jupyter Notebook for whatever that's worth. And we're used at companies like OpenAI, Hugging Face, Cloudflare, BlackRock, universities like Stanford and Berkeley.”
Sutin: Hugging Face started as a teenage chat app before Transformers
“It was a chat app for teenagers. A lot of people don't know that Hugging Face was like, Hey friend, how was school? Let's trade selfies. But then you know, they built the Transformers library, I believe to help them make their chat app better. And then they op…”