When I finished learning how to build an LLM from scratch, I was left with a mystery: my own models were not as good as OpenAI's original GPT-2 models, despite being based on the same architecture. My models all had 163M parameters, and followed the design from Sebastian Raschka's book "Build a Large Language Model (from Scratch)". That meant that they were pretty much the same as the setup for the OpenAI GPT-2 "small" instance, except that they did not use weight-tying or bias on the QKV matrices. Weight-tying means that you re-use the initial embedding matrix as the output head at the end, and using it means that GPT-2 small saved quite a few parameters -- it was 124M rather than 163M -- at, at least in my own experiments, a cost in quality; similarly, while I found that QKV bias made a tiny improvement in loss terms, I'd felt it was likely within the noise. But GPT-2 small consistently beat my models on an instruction fine-tuning (IFT) task -- also adapted from Raschka's book. That test fine-tunes the model on a subset of the Alpaca dataset, until validation loss starts rising, and then runs a test set through the resulting model. The responses to the test set questions are stored, and then I run all of the responses from all of the models under test past GPT 5.5 in one go to get an aggregate score; more details here. GPT-2 small always did better than any of my models on this. Additionally, it did surprisingly well on a simpler eval -- one that just measured the cross entropy loss it got on a test set. It scored close to my own best models, and better than many of them. What made this result particularly interesting was that the test set in question was a split of my own training data; my models would not have seen it when training (at least, in theory), but it seems likely that it would be much more similar to their own training data than it was to OpenAI's. I've checked two things while probing this mystery: It seems very likely that the GPT-2 models were overtrained by modern standards; would overtraining my own models get them closer? It turned out that no, it probably didn't help with the IFT eval (though there might have been some signal there). It did help quite a lot with the test loss eval, though. The way I was handling dropout in the IFT test might have been unduly benefiting some models while working against others. I decided to standardise on not using dropout during this eval, as (counter-intuitively for me) it seemed to harm the results of most models, even those that had been pre-trained with dropout. In particular, the OpenAI weights were harmed by using dropout, and making a change that benefited them (along with some of my own models) seemed the most conservative approach to take in investigating this. The next thing I wanted to look into was the training data. The exact dataset that the various GPT-2 models were trained on has never been released; all we know about it is from the paper, where they say: [W]e created a new web scrape which emphasizes document quality. To do this we only scraped web pages which have been curated/filtered by humans. Manually filtering a full web scrape would be exceptionally expensive so as a starting point, we scraped all outbound links from Reddit, a social media platform, which received at least 3 karma. This can be thought of as a heuristic indicator for whether other users found the link interesting, educational, or just funny. They called it "WebText". There is an OpenWebText that tries to replicate it, but although they tried to follow the same procedure as the original, there's no guarantee that it is all that similar. By comparison, I'd normally been training against FineWeb. While this is a general web-scraping dataset, without the "curation" provided by using only stuff that was linked from upvoted Reddit posts, it has been refined to remove any obvious junk. I had felt that it was pretty much equivalent. But what if I were wrong about that? I decided to see if I could get better models by using better data. The starting point Here's a table of all of the models I've been comparing to date. The "Test loss" column shows how well the model in question did on that held-back cross entropy loss evaluation. The "IFT epochs" column shows how many epochs of fine-tuning the model needed before its validation loss started rising, the "IFT score" the score that GPT 5.5 gave the model's responses to the test set of my Alpaca data, and the "IFT rank" the model's rank in terms of that score. The OpenAI small model is in there in bold, and I've also included the OpenAI medium model for comparison purposes. Test loss IFT epochs IFT score IFT rank OpenAI weights: medium 3.231442 2 43.75 1 JAX, overtrained one long epoch 3.324953 3 19.77 4 JAX, overtrained two normal epochs 3.326482 4 19.72 5 JAX, with MHA bias, no dropout 3.418784 4 18.69 6 JAX, no MHA bias, no dropout 3.420089 5 21.46 3 JAX, no MHA bias, with dropout 3.476802 5 13.22 15 OpenAI weights: small 3.499677 2 26.00 2 1xrtx3090-stacked-interventions 3.538161 4 13.77 14 8xa100m40-stacked-interventions-1 3.577761 4 10.76 18 Cloud FineWeb, 8x A100 40 GiB 3.673623 3 17.72 7 1xrtx3090-baseline 3.683835 4 15.74 8 8xa100m40-baseline 3.691526 3 14.19 13 Cloud FineWeb, 8x H100 80 GiB 3.724507 4 14.33 12 Cloud FineWeb, 8x A100 80 GiB 3.729900 3 11.34 17 Cloud FineWeb, 8x B200 160 GiB 3.771478 4 14.67 11 Local FineWeb train 3.943522 5 12.31 16 Local FineWeb-Edu extended train 4.134991 5 15.04 9 Local FineWeb-Edu train 4.166892 5 14.99 10 You can see that the OpenAI small model did pretty well in terms of the test loss, when you consider that it has 39M fewer weights than my models and was being tested against a dataset that differs more from its likely training data than it does from my own models'. Additionally, the specific models that did better than OpenAI's small one were all trained with JAX rather than PyTorch -- my hypothesis for that is that it's a result of the JAX ones getting better initial weights by pure chance. But the big difference was in the IFT score. In the specific run that gave the results in this table, the OpenAI small model got 26.00 -- the closest of my own models was more than 4.5 points lower, at 21.46. This difference was consistent over all of my other test runs. The GPT-2 small model was always ahead of mine. (GPT-2 medium, of course, beat GPT-2 small and all of my models, but given that it is twice the size of mine, that's not a big surprise.) Now, quite some time ago, I had tried looking into data quality as a lever to pull for model performance. At the bottom of the table, with the worst test loss of all models, you can see two models: "Local FineWeb-Edu train" "Local FineWeb-Edu extended train" These two were (as you might guess from the names) trained on the FineWeb-Edu dataset, which includes just the most "educational" data from FineWeb. They scored very badly on the test loss score. Given that the test dataset is from FineWeb, that's not a big surprise -- as I've written previously: If you train a model on Jane Austen and then evaluate against Chuck Tingle, then you're not going to get amazing results. But again, GPT-2 had the same issue, and did perfectly well on the test loss eval. On the other hand, while these FineWeb-Edu models' performance on the IFT eval wasn't stellar -- there are plenty of my other models ahead of them -- they did seem to punch above their weight. Consistently across all of the IFT evals I've done, they have scored higher than many of the others -- despite their poor loss on the test eval. Additionally: they were amongst the first models that I trained, before I'd spent time learning about how to optimise my hyperparameters and training loop. They did not use gradient clipping, they did use dropout, their batch size was just "whatever I could squeeze into the GPU", and I didn't set the learning rate to the right kind of value or schedule it over the course of the training run. So maybe a new training run on FineWeb-Edu plus my training improvements would help? And maybe some other tweaks to the training data would be worth looking into? The plan I decided to see what would happen if I trained some models with better-quality data. Specifically, I would train models with my current optimised loop and hyperparameters on four different datasets: FineWeb-Edu -- essentially the same as "Local FineWeb-Edu train" but with a better training setup. This would test the "more educational -> better" hypothesis. A 50:50 split of FineWeb and FineWeb-Edu. I've read that LLMs can be helped by having a decent amount of lower-quality data in their training loop, as it helps them to generalise. Perhaps having some FineWeb in there in addition to the FineWeb-Edu stuff would improve that test loss score while also helping the IFT test? A "curated" dataset containing 45% of its contents from FineWeb, 45% from FineWeb-Edu, and 10% from the Simple English Wikipedia. The full Wikipedia is huge, and full of obscure facts -- while the Simple English one is small and hopefully richer in useful information on a per-token basis. And conveniently, Answer.ai have made a snapshot of it available on Hugging Face Hub. Might deliberately putting a bunch of encyclopaedic data into the training set make the model better at the IFT eval (which has lots of factual questions in it, like "who wrote Pride and Prejudice")? OpenWebText. Even though I was unsure how well it matched the original WebText, given that it was there, it seemed silly to not try training something on it and see how it matched up. I would train each model on 3.2B tokens of the chosen dataset; that's the Chinchilla-optimal amount for my 163M-parameter models. If there were any interesting results, then I might consider doing overtrained models later on. I decided to be at least vaguely scientific about this, and to pre-register some predictions: The FineWeb-Edu-only model would do pretty badly on the test loss, but better than my older FineWeb-Edu models (90%). It would also punch above its weight on the IFT eval (90%). The 50:50 split: I expected it to do worse on the test eval than my JAX FineWeb-only models (70%), but better than the FineWeb-Edu one (90%). I wasn't sure about how it would do on the IFT eval, but thought it might be somewhere in between the two groups (60%). The curated dataset I had high hopes for in terms of the IFT eval -- let's say 80% chance of it being the best of all of my models. For the test loss eval, I expected it to do about as well as the 50:50 split, maybe a little bit worse (70%). I had no idea how the OpenWebText eval would do! Could be worse, could be better. Here's how things turned out. The FineWeb-Edu model I already had a dataset based on FineWeb-Edu ready to go, from when I trained those two original models. It is just the 10B-token sample of the original dataset at the time I generated it last December, formatted appropriately for my training script (details on the dataset card). I kicked off a training run with my JAX code (which I've been using for the other posts in this series): giles@poppy:~/Dev/jax-gpt2-from-scratch (main)$ XLA_PYTHON_CLIENT_MEM_FRACTION=0.95 uv run train.py full-llm-full-train-with-mha-output-bias-fineweb-edu datasets/ 2026-09-11 18:11:47.991583 Downloading dataset Fetching 4 files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 1772.93it/s] Download complete: : 0.00B [00:00, ?B/s] | 0/4 [00:00<?, ?it/s] 2026-09-11 18:11:48.226273 Loading dataset into RAM Download complete: : 0.00B [00:00, ?B/s] 2026-09-11 18:16:29.507646 Creating model 2026-09-11 18:16:33.042509 Creating optimizer 2026-09-11 18:16:34.138990 Start train 0%| | 0/33165 [00:00<?, ?it/s] 2026-09-11 18:17:38.486288 Saving checkpoint 1%|▌ | 173/33165 [13:22<39:17:03, 4.29s/it, loss=6.897, tps=21,201] ...and just less than 40 hours later, I had a model: Training complete in 142,912.226 seconds 2026-09-13 09:58:26.437276 Tokens seen: 3,260,252,160 2026-09-13 09:58:26.437284 Throughput: 22,813 tokens/second 2026-09-13 09:58:26.437302 Final train loss: 3.342 2026-09-13 09:58:26.437309 Done I converted the saved JAX safetensors file from the last checkpoint into a format that would be compatible with my PyTorch eval code, and ran my smoke test: how would it complete the sentence "Every effort moves you"? Every effort moves you closer to God’s Kingdom, and even closer to Him. As we can see in That was nice and coherent -- if unusually religious! -- so that was promising. I ran the test eval: giles@perry:~/Dev/ddp-base-model-from-scratch (main)$ uv run test_loss.py datasets/ ../jax-gpt2-from-scratch/runs/full-llm-full-train-with-mha-output-bias-fineweb-edu/model.json ../jax-gpt2-from-scratch/runs/full-llm-full-train-with-mha-output-bias-fineweb-edu/checkpoints/latest/pytorch-model.safetensors Fetching 4 files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 2758.50it/s] 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3200/3200 [03:52<00:00, 13.74it/s] Loss against our test dataset: 3.632900 That was pretty good, putting it at a better test loss than all of the models I had trained without optimised hyperparameters, and worse than all of the ones I had trained on FineWeb with optimised hyperparameters. So that fit in with my prediction that it would be better than the old FineWeb-Edu models; the fact that it was also better than the non-optimised training runs with FineWeb seemed sensible enough that I felt silly for not having predicted that it would have fallen exactly there :-) I decided to leave the IFT eval until the end so that I could check all of the models from these experiments together, so it was time to upload this one to Hugging Face, and move on to the next model. 50:50 FineWeb to FineWeb-Edu I put together a new repo with a script to prepare datasets specifically for my training setup. You provide it with config that specifies some source datasets along with information about how to process them and how to mix them together, and it uploads a new dataset to Hugging Face Hub with the required characteristics. For example, for the 50:50 FineWeb to FineWeb-Edu split, the config looked like this: { "seed": 42, "tokens_desired": 10000000000, "upload_dataset_name": "gpjt/fw-fwedu-5050-gpt2-tokens", "sources": [ { "name": "FineWeb", "hf_id": "HuggingFaceFW/fineweb", "hf_name": "sample-10BT", "hf_split": "train", "item_field": "text", "weight": 50 }, { "name": "FineWeb-Edu", "hf_id": "HuggingFaceFW/fineweb-edu", "hf_name": "sample-10BT", "hf_split": "train", "item_field": "text", "weight": 50 } ] } The way the script works is pretty simple: it works out (based on those weights and the tokens_desired) how many tokens it wants from each source dataset, shuffles the items in the sources, then it loops until it has the desired number of tokens or more stored in an output. In the loop, it works out which source is currently most under-represented, grabs an item from it, tokenises it, and adds it to the output. Running it with that 50:50 config seemed to work fine: giles@perry:~/Dev/prepare-llm-training-dataset (main)$ uv run prepare-dataset.py runs/fw-fwedu-5050/ Resolving data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████| 27468/27468 [00:00<00:00, 89875.56it/s] Loading dataset shards: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████| 102/102 [00:00<00:00, 133.75it/s] Resolving data files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████| 2410/2410 [00:00<00:00, 87461.48it/s] Loading dataset shards: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 98/98 [00:00<00:00, 200.09it/s] 2026-09-13 20:13:22.000187: Generating dataset; per-source counts 2026-09-13 20:13:22.000217: FineWeb: 5,000,000,000 2026-09-13 20:13:22.000221: FineWeb-Edu: 5,000,000,000 FineWeb: 100%|████████████████████████████████████████████████████████████████████████████████████████████████▉| 4999999705/5000000000 [1:01:33<00:00, 1353639.33token/s] FineWeb-Edu: 5000000363token [1:01:33, 1353639.47token/s] 2026-09-13 21:14:55.747239: Done generating tokens 2026-09-13 21:14:55.748480: FineWeb: 4,999,999,705 / 5,000,000,000 (1.000, 1 iterators) 2026-09-13 21:14:55.748487: FineWeb-Edu: 5,000,000,363 / 5,000,000,000 (1.000, 1 iterators) 2026-09-13 21:14:55.748489: Total: 10,000,000,068 2026-09-13 21:14:55.748491: Catting... 2026-09-13 21:16:29.565152: Catted into a tensor of shape torch.Size([10000000068]) 2026-09-13 21:16:29.566663: Saving... 2026-09-13 21:16:36.006267: Saved 2026-09-13 21:16:36.009413: Uploading to gpjt/fw-fwedu-5050-gpt2-tokens Processing Files (1 / 1) : 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████| 20.0GB / 20.0GB, 117MB/s New Data Upload : 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████| 14.6GB / 14.6GB, 98.1MB/s ...du-5050/train.safetensors: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████| 20.0GB / 20.0GB 2026-09-13 21:17:59.545875: Done So we had almost-perfect 50:50 balance between the datasets, and it saved this dataset on Hugging Face. I ran a script to double-check that it looked sane, and it did, so it was time to spin up a training run: giles@perry:~/Dev/jax-gpt2-from-scratch (main)$ XLA_PYTHON_CLIENT_MEM_FRACTION=0.90 uv run train.py full-llm-full-train-with-mha-output-bias-fw-fwedu-5050 datasets/ 2026-09-13 21:20:59.880918 Downloading dataset Fetching 2 files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [01:13<00:00, 36.70s/it] Download complete: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████| 20.0G/20.0G [01:13<00:00, 1.24GB/s] 2026-09-13 21:22:13.521745 Loading dataset into RAM Download complete: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████| 20.0G/20.0G [01:13<00:00, 272MB/s] 2026-09-13 21:22:33.787720 Creating model 2026-09-13 21:22:35.501063 Creating optimizer 2026-09-13 21:22:36.043837 Start train 0%| | 0/33165 [00:00<?, ?it/s] 2026-09-13 21:23:11.437206 Saving checkpoint 0%| | 26/33165 [02:20<38:07:05, 4.14s/it, loss=9.308, tps=18,246] That was running on perry, my normal workstation, and I kicked it off in parallel with the "curated" model training run below on poppy my training box, but I'll keep the runs separate for the purposes of this writeup. When this had been running for an hour or so, our power went out. My guess is that having the tumble dryer running, the car charging, the kettle boiling, the electric hob switched on, and two machines doing training runs is a bit too much for our electrics... which might be a problem in the future, especially if (as planned) I make poppy a multi-GPU machine. However, as things stand, I was able to kick it off again after switching the circuit breaker back on, and things held up. Again, about 40 hours later: Training complete in 136,060.457 seconds 2026-09-15 12:05:26.432638 Tokens seen: 3,227,516,928 2026-09-15 12:05:26.432642 Throughput: 23,721 tokens/second 2026-09-15 12:05:26.432650 Final train loss: 3.793 2026-09-15 12:05:26.432653 Done (Note that the numbers reported at the end of a restarted run like this only include what happened after the restart.) I converted it to PyTorch-compatible tensors, and did the smoke test: Every effort moves you on to other options—in fact, it’s not even worth that effort. Just make Looking good! Time for the loss test: giles@perry:~/Dev/ddp-base-model-from-scratch (main)$ uv run test_loss.py datasets/ ../jax-gpt2-from-scratch/runs/full-llm-full-train-with-mha-output-bias-fw-fwedu-5050/model.json ../jax-gpt2-from-scratch/runs/full-llm-full-train-with-mha-output-bias-fw-fwedu-5050/checkpoints/latest/pytorch-model.safetensors Fetching 4 files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 1192.07it/s] 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3200/3200 [03:53<00:00, 13.72it/s] Loss against our test dataset: 3.462454 That was almost in keeping with my prediction that it would do worse than the JAX FineWeb-only models, except that it was better than the worst of those, "JAX, no MHA bias, with dropout": it was actually better than I predicted. So, a promising model. Time to upload it to Hugging Face -- and now let's move on to the next one. The "curated" dataset With my dataset-preparation script, this was easy enough to set up: { "seed": 42, "tokens_desired": 10000000000, "upload_dataset_name": "gpjt/fw-fwedu-simplewiki-gpt2-tokens", "sources": [ { "name": "FineWeb", "hf_id": "HuggingFaceFW/fineweb", "hf_name": "sample-10BT", "hf_split": "train", "item_field": "text", "weight": 45 }, { "name": "FineWeb-Edu", "hf_id": "HuggingFaceFW/fineweb-edu", "hf_name": "sample-10BT", "hf_split": "train", "item_field": "text", "weight": 45 }, { "name": "Simple English Wikipedia", "hf_id": "answerdotai/simplewiki", "hf_name": "articles", "hf_split": "train", "item_field": "md", "weight": 10 } ] } Running that worked nicely: giles@perry:~/Dev/prepare-llm-training-dataset (main)$ uv run prepare-dataset.py runs/fw-fwedu-simplewiki/ Resolving data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████| 27468/27468 [00:00<00:00, 90196.13it/s] Loading dataset shards: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████| 102/102 [00:00<00:00, 358.90it/s] Resolving data files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████| 2410/2410 [00:00<00:00, 88254.11it/s] Loading dataset shards: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 98/98 [00:00<00:00, 589.23it/s] 2026-09-13 18:59:04.106327: Generating dataset; per-source counts 2026-09-13 18:59:04.106387: FineWeb: 4,500,000,000 2026-09-13 18:59:04.106407: FineWeb-Edu: 4,500,000,000 2026-09-13 18:59:04.106422: Simple English Wikipedia: 1,000,000,000 FineWeb: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████▉| 4499997964/4500000000 [59:41<00:00, 1256362.56token/s] FineWeb-Edu: 4500000607token [59:41, 1256363.31token/s] Simple English Wikipedia: 1000002889token [59:41, 279192.58token/s] 2026-09-13 19:58:45.874744: Done generating tokens 2026-09-13 19:58:45.876043: FineWeb: 4,499,997,964 / 4,500,000,000 (1.000, 1 iterators) 2026-09-13 19:58:45.876048: FineWeb-Edu: 4,500,000,607 / 4,500,000,000 (1.000, 1 iterators) 2026-09-13 19:58:45.876052: Simple English Wikipedia: 1,000,002,889 / 1,000,000,000 (1.000, 6 iterators) 2026-09-13 19:58:45.876054: Total: 10,000,001,460 2026-09-13 19:58:45.876056: Catting... 2026-09-13 20:00:18.811748: Catted into a tensor of shape torch.Size([10000001460]) 2026-09-13 20:00:18.813169: Saving... 2026-09-13 20:00:22.773873: Saved 2026-09-13 20:00:22.773936: Uploading to gpjt/fw-fwedu-simplewiki-gpt2-tokens Processing Files (1 / 1) : 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████| 20.0GB / 20.0GB, 143MB/s New Data Upload : 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████| 19.8GB / 19.8GB, 142MB/s ...plewiki/train.safetensors: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████| 20.0GB / 20.0GB 2026-09-13 20:01:59.270021: Done One thing that is worth noting in that output is the "6 iterators" for the Simple English Wikipedia. If a source dataset runs out of items while we're building up the results in this script, we start iterating over it again (with a different seed for the shuffle so that the ordering is different). The "6 iterators" means that it needed to do that 6 times -- the original creation of the iterator at the start of the script, and five more. So that means that the Simple English Wikipedia is repeated (oversampled) somewhere between five and six times in the dataset. That's not a bad thing! From what I've read, it's actually quite standard to oversample highly educational content in LLM training datasets. And anyway, the dataset the script generated was 10B tokens, of which we're only using 3.2B for the training run in this post, so it would only appear somewhere between one and two times. The repetition would likely only really cut in if and when we did an overtrained model on the dataset. Anyway, I ran my check against the uploaded dataset -- the first few items were clearly from FineWeb, FineWeb-Edu, and the Simple English Wikipedia. It was time to kick off a training run: giles@poppy:~/Dev/jax-gpt2-from-scratch (main)$ XLA_PYTHON_CLIENT_MEM_FRACTION=0.95 uv run train.py full-llm-full-train-with-mha-output-bias-fw-fwedu-simplewiki datasets/ 2026-09-13 20:24:48.037024 Downloading dataset Downloading (incomplete total...): 0.00B [00:00, ?B/s] Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads. | 0/2 [00:00<?, ?it/s] WARNING:huggingface_hub.utils._http:Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads. Fetching 2 files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [02:51<00:00, 85.85s/it] Download complete: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████| 20.0G/20.0G [02:51<00:00, 435MB/s] 2026-09-13 20:27:39.934884 Loading dataset into RAM Download complete: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████| 20.0G/20.0G [02:51<00:00, 116MB/s] 2026-09-13 20:31:20.492877 Creating model 2026-09-13 20:31:24.054143 Creating optimizer 2026-09-13 20:31:25.100832 Start train 0%| | 0/33165 [00:00<?, ?it/s] 2026-09-13 20:32:29.650379 Saving checkpoint 0%|▎ | 107/33165 [08:38<39:05:39, 4.26s/it, loss=7.631, tps=20,293] Again, this was interrupted by the power outage that hit the 50:50 training run, but I was able to restart from a checkpoint. After another 22 hours, it crashed with an error that I've seen before: jax.errors.JaxRuntimeError: INTERNAL: CUDA error: Failed to end stream capture: CUDA_ERROR_STREAM_CAPTURE_INVALIDATED: operation failed due to a previous error during capture [executable_name='jit_train_step'] I put it aside as a one-off oddity when I hit it last time, but this time I dug in a bit more. I noted that it had not ever happened on perry, but seemed to be an issue on poppy, and that poppy had an older version of CUDA and the Nvidia drivers -- might that be the cause? I decided to upgrade those before kicking off the next run, but for now just restarted the run from the most recent checkpoint. (Note for anyone who is hitting the same error: it has not occurred since the upgrade, so that's worth trying.) This time it completed OK: Training complete in 59,564.515 seconds 2026-09-15 15:56:52.909888 Tokens seen: 1,367,212,032 2026-09-15 15:56:52.909894 Throughput: 22,953 tokens/second 2026-09-15 15:56:52.909912 Final train loss: 3.332 2026-09-15 15:56:52.909959 Done Again, these numbers just show what happened after the most recent restart. I copied it over to perry, converted it into a format that was compatible with my PyTorch code, and ran the smoke test: Every effort moves you by the air, for it will make you a better athlete, so your body becomes bigger and stronger Coherent enough -- time for the loss eval: giles@perry:~/Dev/ddp-base-model-from-scratch (main)$ uv run test_loss.py datasets/ ~/Dev/jax-gpt2-from-scratch/runs/full-llm-full-train-with-mha-output-bias-fw-fwedu-simplewiki/model.json ~/Dev/jax-gpt2-from-scratch/runs/full-llm-full-train-with-mha-output-bias-fw-fwedu-simplewiki/checkpoints/latest/pytorch-model.safetensors Fetching 4 files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 1007.64it/s] 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3200/3200 [03:57<00:00, 13.48it/s] Loss against our test dataset: 3.542460 Again, in line with my predictions -- worse than the JAX FineWeb-only models, and indeed than the very best PyTorch one, 1xrtx3090-stacked-interventions, and also worse than the 50:50 split, but better than the FineWeb-Edu one. I uploaded it to Hugging Face, and it was time to move on to what was meant to be the final model for this set of experiments. The OpenWebText run Again, this was a simple enough config to set up: { "seed": 42, "tokens_desired": 10000000000, "upload_dataset_name": "gpjt/openwebtext-gpt2-tokens", "sources": [ { "name": "OpenWebText", "hf_id": "Skylion007/openwebtext", "hf_name": "plain_text", "hf_split": "train", "item_field": "text", "weight": 50 } ] } ...and the build and upload process worked well (and took much less time -- for some reason, sampling randomly from a single dataset is faster than sampling from two or three): giles@perry:~/Dev/prepare-llm-training-dataset (main)$ uv run prepare-dataset.py runs/openwebtext/ Resolving data files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 80/80 [00:00<00:00, 32723.26it/s] Resolving data files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 80/80 [00:00<00:00, 97940.55it/s] Loading dataset shards: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████| 80/80 [00:00<00:00, 1200.13it/s] 2026-09-15 13:16:47.622617: Generating dataset; per-source counts 2026-09-15 13:16:47.622645: OpenWebText: 10,000,000,000 Resolving data files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 80/80 [00:00<00:00, 45602.65it/s] Resolving data files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 80/80 [00:00<00:00, 67650.06it/s] Loading dataset shards: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 80/80 [00:00<00:00, 307.11it/s] OpenWebText: 10000000024token [31:46, 5246208.64token/s] 2026-09-15 13:48:33.761350: Done generating tokens 2026-09-15 13:48:33.762021: OpenWebText: 10,000,000,024 / 10,000,000,000 (1.000, 2 iterators) 2026-09-15 13:48:33.762026: Total: 10,000,000,024 2026-09-15 13:48:33.762028: Catting... 2026-09-15 13:49:33.115508: Catted into a tensor of shape torch.Size([10000000024]) 2026-09-15 13:49:33.115923: Saving... 2026-09-15 13:49:36.365978: Saved 2026-09-15 13:49:36.366027: Uploading to gpjt/openwebtext-gpt2-tokens Processing Files (0 / 1) : 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████▉| 20.0GB / 20.0GB, 147MB/s New Data Upload : 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████| 19.9GB / 19.9GB, 147MB/s ...webtext/train.safetensors: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████▉| 20.0GB / 20.0GB 2026-09-15 13:51:16.202890: Done Note that it needed to oversample -- that "2 iterators". OpenWebText is about 40 GiB uncompressed, and so that's about 10B GPT-2 tokens -- presumably just a little bit less. Again, given that I was planning to use just the first 3.2B tokens of the dataset, I didn't feel that it would matter. I ran the check script on the newly-uploaded Hugging Face dataset and all looked well, so that was all set for the training run. I upgraded poppy first with a sudo pacman -Syu to see if that helped with the weird error that I got in the previous run (which, as I said, it looks like it did), then kicked it off: giles@poppy:~/Dev/jax-gpt2-from-scratch (main)$ XLA_PYTHON_CLIENT_MEM_FRACTION=0.95 uv run train.py full-llm-full-train-with-mha-output-bias-openwebtext datasets/ 2026-09-15 16:42:32.606185 Downloading dataset Fetching 2 files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 941.38it/s] Download complete: : 0.00B [00:00, ?B/s] | 0/2 [00:00<?, ?it/s] 2026-09-15 16:42:32.879987 Loading dataset into RAM Download complete: : 0.00B [00:00, ?B/s] 2026-09-15 16:45:40.438791 Creating model 2026-09-15 16:45:43.840269 Creating optimizer 2026-09-15 16:45:44.848351 Start train 0%| | 0/33165 [00:00<?, ?it/s] 2026-09-15 16:46:50.632075 Saving checkpoint 1%|█ | 332/33165 [24:33<38:45:54, 4.25s/it, loss=6.623, tps=22,154] About 31 hours in, it crashed again, but this time it was my own dumb fault: poppy has a relatively small disk and I ran out of space. I fixed that and kicked it off again from the most recent checkpoint, and this time it completed: Training complete in 33,927.995 seconds 2026-09-17 11:25:10.835989 Tokens seen: 779,747,328 2026-09-17 11:25:10.835994 Throughput: 22,982 tokens/second 2026-09-17 11:25:10.836012 Final train loss: 3.165 2026-09-17 11:25:10.836018 Done I converted it to PyTorch for the smoke test: Every effort moves you through each phase, so it's not a complete picture. I'm sure your story was ...which looked solid, so it was time for the test loss eval: giles@perry:~/Dev/ddp-base-model-from-scratch (main)$ uv run test_loss.py datasets/ ~/Dev/jax-gpt2-from-scratch/runs/full-llm-full-train-with-mha-output-bias-openwebtext/model.json ~/Dev/jax-gpt2-from-scratch/runs/full-llm-full-train-with-mha-output-bias-openwebtext/checkpoints/latest/pytorch-model.safetensors Fetching 4 files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 674.76it/s] 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3200/3200 [03:59<00:00, 13.37it/s] Loss against our test dataset: 4.045255 Our worst score yet in this experiment! Worse than any of my models so far, apart from the two FineWeb-Edu ones I did without optimised hyperparameters. Now, the first draft of this post went straight to the results from here, but the story wasn't quite over yet... Test set contamination GPT-6 Astra is relentless. Before I publish any of these posts, I run them past an editorial board of LLMs to look for issues. GPT-6 Astra not only checked the text, it also visited the code I'd linked to to check that out too, and spotted something problematic. It's obvious in retrospect, but my code to build the new datasets had a high risk of including the contents of the -- in theory held-back -- test set. The way that the test set was generated was that I downloaded the 10B sample of FineWeb back in December, splitting it into 99% training data and 1% "validation". That validation split was about 100M tokens, and I was only using the first 19M or so for actual validation runs during training, so I (somewhat arbitrarily) designated about 19M other tokens starting at position 50M in there as my test set. Now, my new dataset-generation code was just sampling randomly from the complete 10B sample of FineWeb. So there was nothing stopping it from pulling in data that was in that old validation split! That meant that it was quite likely that my new "curated" and "50:50" datasets contained at least some of the test set that was meant to have been held back from the models during training. On reflection, the problem was potentially even worse. FineWeb-Edu is a subset of FineWeb; my existing FineWeb-Edu dataset came from the 10B sample of the Hugging Face original, and so it also could potentially contain documents that I'd put into the test set. The first thing to do was to establish the size of the problem. I wrote a script to take in a "forbidden" dataset and split; this was assumed to be formatted as one big tensor of GPT-2 tokens, which is what all of my datasets are. It would then split it by end-of-text tokens, and generate a hash and a token count for each resulting "document". Optionally, you could restrict it to only considering a subset -- the n tokens starting at position p -- and it would then generate hashes/lengths for the documents inside that slice, or that overlapped it at the start or the end. I ran that to generate a list of hashes for the entire validation set -- the validation split of gpjt/fineweb-gpt2-tokens -- and then used a second script to check my various training sets (and the validation set itself) to see how much of a contamination problem there was. I got these results: Dataset Split Contamination with validation set gpjt/fineweb-gpt2-tokens validation 102163003 out of 102163003 tokens (100.00%) gpjt/fineweb-gpt2-tokens train 636166 out of 102163003 tokens (0.62%) gpjt/fineweb-edu-gpt2-tokens train 672189 out of 102163003 tokens (0.66%) gpjt/fw-fwedu-5050-gpt2-tokens train 49224580 out of 102163003 tokens (48.18%) gpjt/fw-fwedu-simplewiki-gpt2-tokens train 44233824 out of 102163003 tokens (43.30%) gpjt/openwebtext-gpt2-tokens train 212 out of 102163003 tokens (0.00%) So: The validation set was 100% "contaminated" with itself, which was a useful sanity check. The training set of gpjt/fineweb-gpt2-tokens had what I felt was a small level of contamination. It was interesting that there was any at all -- I think that must mean that there are some repeated documents in the original dataset, and some of them wound up with copies in both my training and validation splits. The gpjt/fineweb-edu-gpt2-tokens dataset also had what felt like a reassuringly low level of contamination. Both gpjt/fw-fwedu-5050-gpt2-tokens and gpjt/fw-fwedu-simplewiki-gpt2-tokens, however, looked problematic. In both cases, the training datasets had more than 40% of the validation/test set in them. gpjt/openwebtext-gpt2-tokens was, as you'd expect, almost completely uncontaminated. It looks like maybe one document happened to have been picked up by both the OpenWebText and the FineWeb crawls and then included in the bit of FineWeb I was using for validation. However, these numbers -- while scary, at least for the 50:50 and the curated datasets -- were not quite the ones to use. They showed how much of the full validation set showed up in the full training set; what I actually cared about was how much of the test set -- those 19M tokens starting at position 50M in the validation split -- was in the actual subset of the training datasets that I actually trained on -- the first ~3.2B of them. I re-ran the script to generate hashes for just the test set, and then re-ran the contamination-checking script, telling it just to look at the appropriate subset of the training tokens, and got this: Dataset (first 3.2B tokens only) Split Contamination with test set gpjt/fineweb-gpt2-tokens train 26557 out of 19632681 tokens (0.14%) gpjt/fineweb-edu-gpt2-tokens train 32079 out of 19632681 tokens (0.16%) gpjt/fw-fwedu-5050-gpt2-tokens train 2986889 out of 19632681 tokens (15.21%) gpjt/fw-fwedu-simplewiki-gpt2-tokens train 2682430 out of 19632681 tokens (13.66%) gpjt/openwebtext-gpt2-tokens train None It was clear that there was a problem -- certainly with gpjt/fw-fwedu-5050-gpt2-tokens and gpjt/fw-fwedu-simplewiki-gpt2-tokens. They'd seen what felt like a significant amount of the test set while training, so their results on the test loss eval were dubious at best. I decided to train those two models afresh, and see what the result was in terms of loss. If the difference was huge, I'd look into the risks of the (much smaller) contamination of gpjt/fineweb-gpt2-tokens and gpjt/fineweb-edu-gpt2-tokens. But if it was pretty small, I'd not worry about that too much. I extended the script that prepared datasets so that the config file could specify a forbidden_dataset. Any documents in the source datasets that matched forbidden ones would be excluded from the output. I then updated the config for gpjt/fw-fwedu-5050-gpt2-tokens and gpjt/fw-fwedu-simplewiki-gpt2-tokens so that the whole validation split of gpjt/fineweb-gpt2-tokens was forbidden, and re-generated them. You can see the updated datasets here and here. Running the contamination-checker script against them showed that they were clear. I then re-did the full training runs for those models; the uncontaminated version of the 50:50 split model is here, and the curated one is here. And the good news: both of them actually did very slightly better at the test loss eval than their equivalents that had been trained on the contaminated data: Model Contaminated Test loss JAX, FineWeb/FineWeb-Edu 50:50 No 3.449257 JAX, FineWeb/FineWeb-Edu 50:50 Yes 3.462454 JAX, curated No 3.534068 JAX, curated Yes 3.542460 There are a number of possibilities that come to mind; perhaps learning from the test set just doesn't happen with tiny 163M models like this, or perhaps while the contaminated models were learning, the benefit they got from that was outweighed by the data that they got instead of the test set data being in some way better for training purposes, at least in terms of the loss eval. But anyway, I felt that if the effect of seeing more than 10% of the test set data during training was so tiny, then the effect of seeing less than 0.2% -- which is what the FineWeb-Edu model in this set of training runs had, as did all of my other FineWeb-only models from previous experiments -- would be even smaller and I'd disregard it. That was excellent news! I didn't need to start all of my experiments from scratch. For the rest of this post, I will include the numbers and results for the contaminated models as well as the uncontaminated ones -- they're interesting for several reasons -- but for future posts I'll skip the contaminated ones. So -- finally! -- let's start digging into the final results. Results Firstly, I think it's worth taking a look at all of the test loss results in context. Here they are in a table, with the new models in bold: Test loss OpenAI weights: medium 3.231442 JAX, overtrained one long epoch 3.324953 JAX, overtrained two normal epochs 3.326482 JAX, with MHA bias, no dropout 3.418784 JAX, no MHA bias, no dropout 3.420089 JAX, FineWeb/FineWeb-Edu 50:50 (uncontaminated) 3.449257 JAX, FineWeb/FineWeb-Edu 50:50 (contaminated) 3.462454 JAX, no MHA bias, with dropout 3.476802 OpenAI weights: small 3.499677 JAX, curated (uncontaminated) 3.534068 1xrtx3090-stacked-interventions 3.538161 JAX, curated (contaminated) 3.542460 8xa100m40-stacked-interventions-1 3.577761 JAX, FineWeb-Edu 3.632900 Cloud FineWeb, 8x A100 40 GiB 3.673623 1xrtx3090-baseline 3.683835 8xa100m40-baseline 3.691526 Cloud FineWeb, 8x H100 80 GiB 3.724507 Cloud FineWeb, 8x A100 80 GiB 3.729900 Cloud FineWeb, 8x B200 160 GiB 3.771478 Local FineWeb train 3.943522 JAX, openwebtext 4.045255 Local FineWeb-Edu extended train 4.134991 Local FineWeb-Edu train 4.166892 I think there's something very clear here: with the new models, the more FineWeb that was in the training mix, the better the model did on this eval. I think I might have been subconsciously expecting that in the predictions I did before running these experiments, but in retrospect it's so incredibly obvious that I feel silly for not mentioning it explicitly! But that tells us something interesting. From the description in the paper, whatever OpenAI did the GPT-2 training run on, it was not like FineWeb. It was probably more similar to OpenWebText -- and yet, that model was the one that performed the worst on this test eval, so if it is more like OpenWebText, there must be some other factor involved. But moving on for now: how about the IFT test -- the one that kicked off all of this work in the first place? I generated a set of IFT responses for all of the new models, and then ran them (plus responses for all of the other models on that table above) past GPT 5.5, and found that one of my new models was getting quite close to the original GPT-2 small weights! So I did four more runs, so that I could get an average. Here are the results -- the "IFT score" is the average across all five runs of the judge, and the "IFT rank" is based on that. The "IFT epochs" was from the original result-generation script. Test loss IFT epochs IFT score IFT rank OpenAI weights: medium 3.231442 2 42.36 1 JAX, overtrained one long epoch 3.324953 3 18.67 7 JAX, overtrained two normal epochs 3.326482 4 18.71 6 JAX, with MHA bias, no dropout 3.418784 4 17.90 8 JAX, no MHA bias, no dropout 3.420089 5 20.50 4 JAX, FineWeb/FineWeb-Edu 50:50 (uncontaminated) 3.449257 4 17.69 9 JAX, FineWeb/FineWeb-Edu 50:50 (contaminated) 3.462454 4 19.30 5 JAX, no MHA bias, with dropout 3.476802 5 13.02 21 OpenAI weights: small 3.499677 2 25.19 2 JAX, curated (uncontaminated) 3.534068 4 16.63 10 1xrtx3090-stacked-interventions 3.538161 4 13.51 19 JAX, curated (contaminated) 3.542460 4 13.58 18 8xa100m40-stacked-interventions-1 3.577761 4 10.19 24 JAX, FineWeb-Edu 3.632900 4 24.56 3 Cloud FineWeb, 8x A100 40 GiB 3.673623 3 16.59 11 1xrtx3090-baseline 3.683835 4 15.15 12 8xa100m40-baseline 3.691526 3 13.64 16 Cloud FineWeb, 8x H100 80 GiB 3.724507 4 13.59 17 Cloud FineWeb, 8x A100 80 GiB 3.729900 3 10.79 23 Cloud FineWeb, 8x B200 160 GiB 3.771478 4 13.70 15 Local FineWeb train 3.943522 5 11.87 22 JAX, openwebtext 4.045255 4 13.28 20 Local FineWeb-Edu extended train 4.134991 5 14.29 14 Local FineWeb-Edu train 4.166892 5 14.69 13 If you want to see the full numbers, they're below. The number that initially surprised me, and made me decide to do multiple LLM-judge runs was the one for the "JAX, FineWeb-Edu" model. In my first run it came in at 24.35 vs the OpenAI small weights' 24.93 -- so close that I wondered if it might even beat them on a re-run. However, in the further four runs its score was consistently lower than the OpenAI model's, and the gap extended a bit in some. So, was FineWeb-Edu the clear winner here? Perhaps. If you look at the contaminated/uncontaminated pairs, something interesting pops out. For the 50:50 mix, the model trained with the contaminated dataset got 19.30, and the one trained on the uncontaminated one got 17.69 -- a difference of 1.61. For the "curated" dataset, the situation was even more interesting: uncontaminated got 16.63, while contaminated got 13.58, a delta of 3.05 points. Remember, the contamination issue is about whether or not the model saw the held-back test set during training. It was an issue for the test loss that is based on that test set, but is entirely orthogonal to the IFT test. From the IFT perspective, both contaminated and uncontaminated models in each case saw training data that was -- in theory, at least -- essentially the same in terms of quality. Indeed, the uncontaminated run saw almost the same data in the same order as the contaminated one, except that some items were omitted, and then extra ones were added to the end. The purpose of this set of experiments was to see how data quality affected the results on the IFT test set. But in the case of the curated model, something that should be unrelated to data quality changed the results by 3.05 points! If something as simple as changing which data of the same quality the model is trained with can affect the IFT score so drastically, it makes it a bit harder to be certain as to whether or not data quality really had the effect we were looking for. On the other hand, the FineWeb-Edu model came in at 24.56, which is 4.06 points better than the 20.50 that the closest other model got -- more than the 3.05 points we see in difference between the two curated dataset models. And it's worth noting that the model with 20.50 is "JAX, no MHA bias, no dropout", which has a subtly different architecture -- no bias on the output projection of the multi-head attention blocks. A better comparison might be "JAX, with MHA bias, no dropout", which got a score of 17.90, for a whacking great difference of 6.66 points. I think that without doing a very large number of training runs on different datasets with different mixes, each one created with a different seed, it would be hard to work out exactly what is in the noise here and what is not. However, that would cost a lot in terms of time. I think that the best thing here is to chalk this up as a fairly decent indication that FineWeb-Edu improves matters for the IFT eval, but far from a certainty. But it's certainly worth noting that whatever the noise is, it has a range of at least 3.05 points -- and the FineWeb-Edu model is just 0.63 points short of GPT-2 small! So there could well be something there. Of course, we don't know whether that model got (by chance) the best possible balance of FineWeb-Edu tokens, and could never win -- or whether it got a bad balance and would actually beat GPT-2 with a better one. So that's certainly worth keeping in mind. As an aside, the result for the curated dataset really surprised me. I had expected that it would be the best one, simply because it almost certainly contained more facts. I took a look at its answers to the questions -- one possibility that came to mind might be that it would get better responses to questions like "What is the chemical symbol for chlorine" or "Who wrote Pride and Prejudice" than the others, but would fail on less knowledge-based tasks. But it was terrible at fact-based questions too: Name the author of 'Pride and Prejudice'. What is the periodic symbol for chlorine? As I understand it, many real-world training runs do include (often oversampled) amounts of highly educational training data like this model's dataset did. But perhaps the models that I'm training are just too small to be able to make use of the data they gained that way -- maybe doing things this way and expecting good results is like asking six-year-old children to memorise stuff before they've learned enough to be able to make use of it 1. It's worth noting that the GPT-2 small model also failed on those factual questions. Well, anyway: I think we have some useful results here, so let's work out what that means for next steps. Conclusion The results we got in these experiments point in two interesting directions. The perfect connection between the amount of FineWeb in the training set and the result on the (FineWeb-based) test loss eval, while perfectly obvious in retrospect, really does highlight how mysterious it is that the OpenAI small weights do so well on that test. The fact that FineWeb-Edu did well on the IFT test tells us that there does seem to be value in using richer training data -- though the less-spectacular results of the 50:50 mix and the curated one weaken that a bit, as does the indicator of what the noise due to data selection from equivalently high-quality datasets might be. The OpenWebText result I think I'll ignore, given that -- while in theory it should be similar to what OpenAI trained on -- there are no guarantees, and it might differ in non-obvious ways for non-obvious reasons. I think that the right direction to take this going forward is to separate these two angles. I should chase a higher IFT score, and then once I have nailed that down, I should see what (if anything) might allow me to get the resulting model to improve its test score. But I will need to make sure that whatever dataset I use, I use various "mixes" of it -- versions created with different random seeds. In my earlier experiments with overtraining, I did find that it didn't seem to improve the IFT results -- but it did improve the test loss. So perhaps identifying the right combination of other factors to boost the IFT score, then overtraining the result, might help? Of course, my overtraining tests were with FineWeb, so the connection might not hold up as well if the starting model (as seems likely) was trained on a different dataset. Also, while working through the results here, I've come to the conclusion that the set of models I'm using is a bit confusing -- there are now different hyperparameter settings, small architectural differences (the MHA bias thing), dropout settings during the pre-training, and now datasets. I think that's OK for now; I should see this part of this series as more ideation than actually running the proper experiments. But at the end, when I have some solid hypotheses with a reasonable amount of backup, I should start from scratch: a baseline model, then staged interventions to build up to what (hopefully) will be a model as good as GPT-2 small. Anyway, I'll wrap this one up here. I think that the next lever to pull is (perhaps surprisingly) going to be weight tying. I had previously kind of disregarded that as a possibility, but while I was working on this post, something popped into my mind. The OpenAI models were originally trained with weight tying. My codebase does actually support doing it -- but because I got the OpenAI weights I'm using from the code in "Build a Large Language Model (from Scratch)", when I'm running the IFT test, the weights are not actually tied! We load up a model that has separate but identical embedding and output head matrices, and then we fine-tune that. So those two matrices can vary independently during fine-tuning -- to put it another way, while GPT-2 small was pre-trained with 124M parameters, the IFT test is being done on a 163M-parameter version. Does that give them some non-obvious advantage? And would adding weight-tying to my own models help, either with or without the output heads being independent at fine-tuning time? Stay tuned :-) Appendix: all IFT judge runs Here are the numbers for all of the IFT judge runs, included for completeness. You can see that the LLM judge ranks models very consistently between runs, but there is variation -- that is, on some runs it's in what I think of as a "better mood" than others, and if that's the case, it will give better scores -- but it will give them almost consistently between models, so all of the models do better. Note that (unlike the table above) this one is sorted by the average IFT score rather than the test loss. Model Run 1 Run 2 Run 3 Run 4 Run 5 Average OpenAI weights: medium 42.24 42.16 42.95 41.83 42.61 42.36 OpenAI weights: small 24.93 24.96 25.39 25.01 25.66 25.19 JAX, FineWeb-Edu 24.35 24.55 24.3 24.68 24.9 24.56 JAX, no MHA bias, no dropout 20.5 19.9 20.76 21.25 20.07 20.50 JAX, FineWeb/FineWeb-Edu 50:50 (contaminated) 19.16 18.86 19.61 19.17 19.7 19.30 JAX, overtrained two normal epochs 18.47 18.29 19.17 18.69 18.91 18.71 JAX, overtrained one long epoch 18.04 18.71 19.62 18.41 18.57 18.67 JAX, with MHA bias, no dropout 17.49 17.35 18.33 17.73 18.62 17.90 JAX, FineWeb/FineWeb-Edu 50:50 (uncontaminated) 17.37 17.73 17.53 18.01 17.83 17.69 JAX, curated (uncontaminated) 16.77 16.03 17.3 16.08 16.96 16.63 Cloud FineWeb, 8x A100 40 GiB 16.44 16.23 17.14 16.62 16.54 16.59 1xrtx3090-baseline 14.85 15.07 15.19 15.14 15.51 15.15 Local FineWeb-Edu train 14.37 14.23 15.08 14.79 15 14.69 Local FineWeb-Edu extended train 14.4 14.07 13.82 14.56 14.61 14.29 Cloud FineWeb, 8x B200 160 GiB 13.37 13.05 13.85 13.67 14.57 13.70 8xa100m40-baseline 13.64 13.36 13.9 13.32 13.97 13.64 Cloud FineWeb, 8x H100 80 GiB 13.45 13.32 13.6 13.51 14.07 13.59 JAX, curated (contaminated) 13.09 13.48 13.95 13.23 14.15 13.58 1xrtx3090-stacked-interventions 13.37 13.11 14.04 13.84 13.17 13.51 JAX, openwebtext 12.88 12.7 13.74 13.53 13.53 13.28 JAX, no MHA bias, with dropout 13.19 12.86 12.98 12.85 13.24 13.02 Local FineWeb train 11.75 11.75 12.21 11.46 12.19 11.87 Cloud FineWeb, 8x A100 80 GiB 10.68 10.2 11.03 10.55 11.49 10.79 8xa100m40-stacked-interventions-1 9.44 9.79 10.84 10.2 10.66 10.19 A small boy asleep on his right side, the right arm stuck out, the right hand hanging limp over the edge of the bed. Through a round grating in the side of a box a voice speaks softly. "The Nile is the longest river in Africa and the second in length of all the rivers of the globe. Although falling short of the length of the Mississippi-Missouri, the Nile is at the head of all rivers as regards the length of its basin, which extends through 35 degrees of latitude …" At breakfast the next morning, "Tommy," some one says, "do you know which is the longest river in Africa?" A shaking of the head. "But don't you remember something that begins: The Nile is the …" "The - Nile - is - the - longest - river - in - Africa - and - the - second - in - length - of - all - the - rivers - of - the - globe …" The words come rushing out. "Although - falling - short - of …" "Well now, which is the longest river in Africa?" The eyes are blank. "I don't know." "But the Nile, Tommy." "The - Nile - is - the - longest - river - in - Africa - and - second …" "Then which river is the longest, Tommy?" Tommy burst into tears. "I don't know," he howls. Brave New World, Aldous Huxley ↩

I hadn't uploaded the models that I trained using JAX to the Hugging Face Hub because Transformers has been PyTorch-only since version 5 (though they say they're working to add interoperability with JAX in the future), so it would have been tough to get them working natively with AutoModelForCausalLM and the like. But then it dawned on me that I'd already written a conversion script that could take my JAX safetensors files and convert them into ones compatible with my PyTorch code. It's actually those converted models that I use for my evals -- so I could use my existing PyTorch script to upload them. So, I've now uploaded PyTorch-compatible versions of all of my JAX-trained models: "Writing an LLM from scratch, part 34b -- from bigrams to GPT-2, one component at a time (in JAX)" gpjt/jax-no-mha-bias-no-dropout -- the first full LLM trained in the post, in the "Adding LayerNorm" section. gpjt/jax-no-mha-bias-with-dropout -- the second full LLM trained in the post, in the "Dropout" section. gpjt/jax-with-mha-bias-no-dropout -- the third full LLM trained in the post, in the "Adding bias to the MHA output projections" section. "Why do OpenAI's GPT-2 weights beat mine? Part three: testing overtraining" gpjt/jax-with-mha-bias-no-dropout-extended -- the single-epoch, double-Chinchilla-tokens model. gpjt/jax-with-mha-bias-no-dropout-2-epoch -- the model trained on two epochs over the Chinchilla-optimal number of tokens. "A quick(ish) Chinchilla check" gpjt/jax-with-mha-bias-larger-chinchilla-1 -- the slightly-larger model. gpjt/jax-with-mha-bias-larger-chinchilla-2 -- the slightly-smaller model. I've also added links to the posts in question.

I recently overtrained a couple of GPT-2 style models, training them both on 40 tokens per parameter rather than the 20 per parameter that is generally regarded as "Chinchilla-optimal". The normal heuristic is that instead of doing that, you should scale up the number of tokens and the number of parameters equally -- so I would have been better off scaling up the model by 2 and the token count by the same amount. By doing that, I should expect to get a better model in terms of loss on my held-back test set than I did with my 40-tokens-per-parameter models. My training machine poppy wasn't doing anything, so I decided to give that a go. Would the Chinchilla rule-of-thumb hold up? As you might expect, it did. But it was a surprisingly close-run thing, and could conceivably have been in the noise. Let's take a look. The Chinchilla heuristic If you already know all about the Chinchilla paper -- regular readers in particular must be sick and tired of it by now :-) -- then click here to skip this section. In "Training Compute-Optimal Large Language Models", which is always called the Chinchilla paper after the name of the model they trained at the end, the authors tried to work out the optimal number of tokens to train an LLM on based on its number of parameters. In particular, they were pushing back on a trend they were seeing at the time, where people were making models ever-larger, but not increasing the amount of data they were training on. The authors were all at Google DeepMind, and this was the kind of project that only a large lab could do: they trained "over 400 language models ranging from 70 million to over 16 billion parameters on 5 to 500 billion tokens". Their conclusion was "for compute-optimal training, the model size and the number of training tokens should be scaled equally: for every doubling of model size the number of training tokens should also be doubled". They don't actually state an overall optimal number of tokens to train on in the paper, but in table 3 they provide an estimate of the optimal training FLOPs and tokens for models of various sizes, and it's approximately 20 tokens per parameter. That number has become a heuristic, and people talk about a model as being trained for the Chinchilla-optimal number of tokens. Models that were trained on fewer tokens per parameter are referred to as "undertrained", and models that were trained on more as "overtrained". It's worth noting that overtraining a model is not, in itself, a bad thing. If you have a model of a particular size and you continue training it past the Chinchilla-optimal number of tokens, it will -- in general -- get better. The point of the heuristic is that doing that is not the best way to spend whatever budget you have in terms of compute time. You'll get better results, as they say, by scaling the number of tokens and the number of parameters equally. But let's say you're creating a model for specific target hardware -- say, a mobile device. You have a hard restriction on how large the model can be -- the device has only so much RAM to hold it. So it might make sense to overtrain to get a better model. 1 But if you're not so limited in how many parameters you can use, then you should indeed scale the model up, and that's what I wanted to try. How would that work? Scaling the model A week or two back, I was investigating whether I could make my GPT-2 style models better at a specific instruction-following task by overtraining them. The details of that experiment aren't important here, but what it meant was that I had three GPT-2-style models, each of exactly the same size, roughly 163M parameters A Chinchilla-optimal one, which I'll call jax-gpt2-chinchilla here. One trained on twice the Chinchilla-optimal tokens, jax-gpt2-2x-chinchilla. One trained on the Chinchilla-optimal tokens, with two epochs (so that it was trained for as long as #2): jax-gpt2-2-epoch-chinchilla When I tested them against a held-back test set of sequences -- stuff that they'd never seen before -- they got results rather like you might expect: Test loss jax-gpt2-2x-chinchilla 3.324953 jax-gpt2-2-epoch-chinchilla 3.326482 jax-gpt2-chinchilla 3.418784 A lower loss is better, and you can see that the longer-trained models were noticeably better than the Chinchilla-optimal one. The difference between them was tiny; they were trained starting with the same initial weights, and the training runs themselves were deterministic, but a difference of 0.05% in loss doesn't seem like it could be meaningful -- an extra batch for one or one fewer for the other could easily swap them around, you'd think. Now, these models each had 163,009,536 parameters -- they were the small-size model from the GPT-2 paper, modified to not have QKV bias or weight-tying. jax-gpt2-chinchilla had been trained on 3,260,190,720 tokens (rounded up to fit into a round number of full batches), and the other two on 6,520,381,440 tokens each -- double the amount (rounded up too). What I needed to do for my Chinchilla check was to try training a model that used the same amount of compute, scaling the parameters and the number of training tokens equally. Because training compute increases roughly linearly with both parameters and tokens, that would mean scaling both up by 2, giving us: 163,009,536*2≈230,530,296parameters ...and thus 4,610,605,920 tokens. How to scale the model up? In the GPT-2 paper, they train four models: Name Parameters 2 Layers d_emb MHA heads 3 small 124M 12 768 12 medium 345M 24 1024 16 large 762M 36 1280 20 xl 1542M 48 1600 25 I wanted to scale my own model up from 163M parameters to about 231M. Which of those numbers would I want to increase, and by how much? The first thing that stands out is that the number of heads is always 1/64th of the number of embedding dimensions. So that sorted that one out. I just needed to adjust the number of layers, and the number of embedding dimensions, but ensure that the latter was a multiple of 64. I decided to see if I could fit some kind of curve to the relationship between the number of parameters and the GPT-2 authors' choices. This was made a bit more complicated by one thing: they were using weight-tying, and I was not. That meant that they re-used the embedding matrix at the start of the LLM as an output head at the end -- which is why they had 38M fewer parameters. Embeddings and the output head make up a surprisingly large percentage of the parameters for small models like this -- about 47% without weight-tying, 23% with. I couldn't work out a solid way to scale things up and wound up doing some rather messy hacking around in a spreadsheet. I came up with two proposed model sizes that were within a couple of percentage points of the right size: Name Layers d_emb MHA heads Parameters % diff slightly-larger 15 896 14 235,621,120 +2.21% slightly-smaller 14 896 14 225,978,368 -1.97% Interestingly, I found that because d_emb could only change in increments/decrements of 64, it was a pretty coarse control -- my first attempt at making a slightly-smaller model changed it to the next step down, 832, but that led to a model that was 9.25% too small. That was an interesting first lesson. I'd previously been thinking of the Chinchilla rule as being something like "don't double the tokens, just scale the model and the tokens equally". But that "just" was wrong. Scaling a model is hard -- even with just two dials to fiddle with, like in this case, it was tricky to get something right -- and I can't say for sure that my choices were the right ones. Anyway, the next step was to double-check that these models would use the right amount of compute to train. Training FLOPs As I said earlier, the compute time scales roughly linearly with the number of parameters. Let's dig into that "roughly". Different kinds of parameters take different amounts of FLOPs to train, and scale differently with things like the embedding dimensions, sequence length, and so on. Now, for very large models, a lot of that comes out in the wash, but with tiny models like these where the embeddings make up such a large proportion of the parameters, it might matter. Conveniently, in appendix F of the Chinchilla paper, they provide a set of formulae for estimating the number of training FLOPs for a normal dense LLM like these ones. I coded that up into a script that, given the JSON configuration files I was using for my models and training runs, would work out the number of FLOPs for a single epoch of training. It didn't take account of the fact that my real training runs round the number of tokens up so that we do a round number of full batches, but I felt that so long as the results weren't very close that wouldn't matter. I got these results (multiplying the two-epoch numbers by two): Est. training FLOPs jax-gpt2-chinchilla 3,544,967,596,946,227,200 jax-gpt2-2x-chinchilla 7,089,935,193,892,454,400 jax-gpt2-2-epoch-chinchilla 7,089,935,193,892,454,400 slightly-larger 7,419,664,885,127,577,600 slightly-smaller 6,804,429,215,367,168,000 The numbers were indeed different enough that I wasn't worried about the batch-rounding. And the good news was that slightly-larger and slightly-smaller would indeed use slightly more and slightly less compute to train than the overtrained models -- about 4.6% more and 4% less respectively. A true Chinchilla-equivalent model would lie somewhere between them. It was time to train some models! Training I kicked off the run for the slightly-larger model first. Because it was bigger than the 163M models I'd been training, I couldn't fit such large batches into my VRAM; previously I'd been running with a batch size of 6, and now I could only fit in a batch of 4. Luckily, though, I was using gradient accumulation, so by bumping that up from 16 steps to 24 steps I could keep the same overall batch size and keep the training runs comparable. Even despite that, the training run ran out of VRAM about 60 hours in -- I'm guessing due to VRAM fragmentation, as I did not have TF_GPU_ALLOCATOR set to cuda_malloc_async -- but I was able to restart from the most recent checkpoint and complete the run. After just less than four days total training time, it completed. When it was done, I copied the last checkpoint 4 over to my dev box, perry, and ran my standard smoke test against it, asking it to complete "Every effort moves you" with 20 tokens, using greedy sampling. I got something reasonably coherent: Every effort moves you. I’m not sure what you’re thinking. I’m Next, I converted the safetensors file -- which had been saved by my JAX code -- into a format compatible with my PyTorch code, because that's what I use for evals. I ran another smoke test (this one with temperature 1): Every effort moves you through the motions for your life, your soul, your body, and your soul’s happiness Very spiritual. Next, it was time to work out the loss on my held-back test set: giles@perry:~/Dev/ddp-base-model-from-scratch (main)$ uv run test_loss.py datasets/ ../jax-gpt2-from-scratch/runs/full-llm-full-train-with-mha-output-bias-larger-chinchilla/model.json ../jax-gpt2-from-scratch/runs/full-llm-full-train-with-mha-output-bias-larger-chinchilla/checkpoints/latest/pytorch-model.safetensors Fetching 4 files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 3485.09it/s] 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3200/3200 [07:11<00:00, 7.42it/s] Loss against our test dataset: 3.280028 Well, it was certainly better than the 3.324953 that the best of the overtrained models got -- but only by a bit over 1% better. Interesting! I decided to train the second model, slightly-smaller. This one crashed mid-way through with an error that I've seen before: jax.errors.JaxRuntimeError: INTERNAL: CUDA error: Failed to end stream capture: CUDA_ERROR_STREAM_CAPTURE_INVALIDATED: operation failed due to a previous error during capture [executable_name='jit_train_step'] I'm going to have to investigate that more in future, but for now, I just restarted from the checkpoint, and again after a bit less than four days, I had a model. The JAX smoke test was solid: Every effort moves you forward. The best way to get started is to start with a free trial. You can ...and so was the PyTorch one: Every effort moves you forward in love with our products. I love the way it’s easy to use. Both quite commercial this time! It was time for the proper test loss eval: giles@perry:~/Dev/ddp-base-model-from-scratch (main)$ uv run test_loss.py datasets/ ~/Dev/jax-gpt2-from-scratch/runs/full-llm-full-train-with-mha-output-bias-larger-chinchilla-2/model.json ~/Dev/jax-gpt2-from-scratch/runs/full-llm-full-train-with-mha-output-bias-larger-chinchilla-2/checkpoints/latest/pytorch-model.safetensors Fetching 4 files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 3151.24it/s] 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3200/3200 [06:45<00:00, 7.90it/s] Loss against our test dataset: 3.292937 So, slightly worse than the 3.280028 from the larger model, better than the 3.324953 from the best overtrained one. Time to put this all together. Results Here's an updated version of the table from the start of this post; I've added in the two new models, and the improvement they each had over jax-gpt2-2x-chinchilla in both absolute terms and as a percentage rounded to 3sf. Test loss Improvement Improvement % slightly-larger 3.280028 0.044925 1.35% slightly-smaller 3.292937 0.032016 0.962% jax-gpt2-2x-chinchilla 3.324953 - - jax-gpt2-2-epoch-chinchilla 3.326482 - - jax-gpt2-chinchilla 3.418784 - - Now, unlike the overtrained models, prior to training these two new ones started with different initial weights to the jax-gpt2-chinchilla one -- after all, they had to, because they had more of them! A while back, I did a bit of analysis of how random variation in weight initialisation can change the resulting test loss. It wasn't anything in-depth, but I trained three models with different explicit seeds set prior to the model initialisation, but with the same seed set before the training run started 5. Those three models wound up with test losses of 3.681356, 3.673943, and 3.664345. Doing statistics with three data points is a bit flaky, but the cost of training models is so high that I'll leave the Proper Science to the likes of Google DeepMind and wing it :-) Mean: ~3.673215 Sample variance: ~0.000073 Standard deviation (SD): ~0.008529 Now, piling statistical flakiness on statistical flakiness, we'll compare these. You'd normally expect about two thirds of results to be within one SD of the mean, 95.4% to be within two SDs, and 99.7% to be within three. Three SDs on that (yes, different, I know) distribution is 0.025587. That's smaller than both of the improvements that our Chinchilla-optimal runs had over the overtrained ones. So what does that tell us? Well, perhaps not much given the statistical flakiness. But I think it is useful directionally. It suggests that we might be able to take these results seriously as an improvement, and that Chinchilla held: scaling up the model and the number of tokens evenly did give us a better model than just scaling up the number of tokens. In particular, the fact that the loss for slightly-smaller was lower -- even though it had 4% less compute spent on it than the overtrained models -- was encouraging. But it's certainly far from a slam-dunk. A larger test, training lots of overtrained models and lots of Chinchilla-optimal ones, all with different random seeds, would give actual real serious data. Not worth it for me, and perhaps not for anyone. Conclusion I wanted to do a quick sanity check of the Chinchilla heuristic of 20 tokens per parameter. I came up with results that were certainly in line with it -- perfectly so in terms of the ordering of the models I trained. But the effect was small enough that I could imagine that it was in the noise, especially given the small numbers of models I'm able to train. I'll chalk it up as a tentative success. In addition, I learned one useful thing: when talking about scaling up a model to more parameters, you actually have to think quite hard about where you want to put those parameters. I wound up doing a rough curve-fit to the models in the GPT-2 paper, but I have no idea if that was optimal. At some point I should try to dig up some research into optimising embedding dimensions, numbers of layers, and so on. But not now, as I've a bunch of other stuff I want to investigate first. Anyway, I hope you found this experiment interesting, and as ever, comments and questions welcome below. Thanks for reading! I'm less familiar with arguments for under-training -- that is, for fewer than 20 tokens per parameter. I've heard that these days, modern LLMs get a lot more reinforcement learning than they do pre-training, and perhaps that might mean that some very big ones are undertrained prior to RL? I'm uncertain. It's unlikely to be raw lack of data; even for those of us outside the big labs, FineWeb has 18.5T tokens. On its own, that would be enough to train a 0.925T-parameter model, and given that you can apparently do four epochs over the same data before you start getting diminishing returns, that takes us up to 3.7T. That's frontier-lab size, and I'm sure they have better datasets than FineWeb. ↩ Parameter counts are from the paper, apart from the "small" model, which is known to be wrong -- I used my own calculation, and the result is in line with what I've seen elsewhere. ↩ The paper doesn't mention the number of heads; these numbers are from "Build a Large Language Model (from Scratch)", and match up with the ones on this Hugging Face page. ↩ Regular readers might have noticed that I'm ignoring what I've been calling the "best" checkpoint. I've come to the conclusion that because for my training script, "best" means best in terms of training loss, and the training loss changes based on what training data the model has seen recently, it's actually not a very useful metric and just confuses things. At some point I'll probably re-introduce pre-checkpoint evals and use that for "best", which would be the right way to do it. ↩ At the time I was using dropout, so training runs were not deterministic without a known seed. ↩

I'm porting my PyTorch LLM code to JAX, using Flax as the neural network layer. For various reasons I wanted to use Safetensors to store checkpoints of the model. It took a little while to get it working; here's the trick I learned. If you look at the Safetensors docs, you'll see that it doesn't mention a JAX implementation -- indeed, searching for "safetensors jax" at the time I'm writing this gives you a link to this GitHub repo by Alvaro Bartolome -- which was last updated in 2023. However, if you look more closely at the docs, they do have a link to the Flax API. I feel this is somewhat misnamed, as it is actually a JAX API. There's no reference (again, as of the time of writing) to Flax in the source -- it's all just JAX code. And in fact Bartolome's library uses it under the hood. There is one problem, though. The API works with simple single-level dictionaries, with strings mapping directly to JAX arrays. For example, the save_file function has this signature: def save_file( tensors: Dict[str, Array], filename: Union[str, os.PathLike], metadata: Optional[Dict[str, str]] = None, ) -> None This can cause problems if you're not careful. If you look at the Flax documentation on checkpointing, it suggests that you use Orbax 1, which has its own API and file format, but then goes on to say: When interacting with checkpoint libraries (like Orbax), you may prefer to work with Python built-in container types. In this case, you can use the nnx.State.to_pure_dict and nnx.State.replace_by_pure_dict API to convert an nnx.State to and from pure nested dictionaries. I initially put two and two together -- that and the dictionary-based API for Safetensors -- and got five, and tried feeding one of those "pure" dicts into Safetensors. I got a very confusing error: SafetensorError: dtype object is not covered It's worth digging in to why that happens. The problem is that although Safetensors is expecting a dict of strings mapping to tensors, it doesn't check that that is what it actually gets. And while the dictionaries from nnx.State.to_pure_dict are "pure", they are also nested (as the docs say!). Even for the simple model I was working with, I got a structure like this: { 'output_head': { 'kernel': Array([...], dtype=float32) }, 'token_embedding': { 'embedding': Array([...], dtype=float32) } } So, we had strings mapping to dicts, and those dicts mapped from strings to the JAX arrays. More complex models would have had deeper dict structures. Now, internally inside Safetensors, the Flax/JAX API is a simple wrapper. It iterates over the keys in the dictionary it's been provided with, and tries to convert their respective values into NumPy arrays. It does that by passing them into NumPy's asarray function, which accepts things like lists, tuples, and NumPy arrays, and converts them into arrays. JAX's own Array class exposes an interface that it recognises, so they're converted without trouble. Once it's done that, it passes the result to a lower-level Rust implementation that actually converts everything to Safetensors format. But because Safetensors didn't check types, in my case it was iterating over the top level of the dict, trying to convert the values to NumPy arrays, and got something like this: { 'output_head': numpy.array({'kernel': Array([...], dtype=float32)}, dtype=object), 'token_embedding': numpy.array({'embedding': Array([...], dtype=float32)}, dtype=object) } That is -- because it assumed that the values in the top-level dict were JAX Arrays, it blindly tried to convert them to NumPy arrays. But they were dicts (that happened to map from strings to arrays) -- and if you ask asarray to create an array based on a random object, it happily does so and wraps that object in a NumPy array, with a dtype of object. When that is then fed into the lower-level Rust code that is trying to write the file, it encounters NumPy arrays that have a dtype it can't handle, object -- hence that error: SafetensorError: dtype object is not covered It all makes sense when you read through the code, but I was a bit perplexed for a while! I think all this might be the reason why Bartolome created his GitHub repo. In the README, he says that: There are no plans from HuggingFace to extend safetensors to support anything more than tensors e.g. FrozenDicts, see their response at huggingface/safetensors/discussions/138. So the motivation to create safejax is to easily provide a way to serialize FrozenDicts using safetensors as the tensor storage format However, you don't need to use that library to serialise simple Flax models. Consider how PyTorch models get serialised to Safetensors; my LLMs have keys with names like out_head.weight, pos_emb.weight, and trf_blocks.0.att.out_proj.weight. They're "flat" dictionaries mapping strings to PyTorch Tensors, similar to what Safetensors wants for these Flax ones, but they use dots to separate different levels, with integers for list items and strings for field names. Looking at the pure-dict structure I had for my model: { 'output_head': { 'kernel': Array([...], dtype=float32) }, 'token_embedding': { 'embedding': Array([...], dtype=float32) } } ...you can see that you could walk the dictionary structure to generate keys like output_head.kernel and token_embedding.embedding. That would be easy enough to code up. But -- as Adithya Dsilva points out on GitHub -- you can get there even faster by using nnx.to_flat_state. That returns a (non-dict) structure like this: FlatState([ (('output_head', 'kernel'), Param( # 786,432 (3.1 MB) value=Array([[ 2.3581974e-02, 3.0957451e-02, -3.5088759e-02, ..., -4.5880198e-02, 5.3717274e-02, -2.6590331e-02], ..., [-9.6302675e-03, -3.3276502e-02, 5.7173111e-02, ..., -7.9063717e-03, 2.0532632e-02, 5.4753982e-02]], dtype=float32) )), (('token_embedding', 'embedding'), Param( # 786,432 (3.1 MB) value=Array([[ 0.00273973, -0.01754938, 0.04656043, ..., -0.04276522, -0.03986642, -0.00781331], ..., [ 0.01421758, -0.0219186 , -0.01701825, ..., -0.00793659, 0.00500103, 0.03839901]], dtype=float32) )) ]) If you iterate over that FlatState, you get tuples where the first element is that tuple of strings, like ('output_head', 'kernel'), and the second is a Param object wrapping the JAX Array. The tuples mirror the dot-separated string format in the PyTorch-style Safetensors files. Param objects also implement an interface that asarray can understand, so you can quickly and easily convert the FlatState to a regular dict for Safetensors: from safetensors.flax import save_file ... model_state = nnx.state(model) flat_state = nnx.to_flat_state(model_state) simple_dict = {} for tuple_key, param in flat_state: key = ".".join(str(key) for key in tuple_key) simple_dict[key] = param save_file(simple_dict, "model.safetensors") (You need to wrap key in a str because if you have a nnx.Sequential in your model, the item in the tuple will get an integer index rather than a string). You can go the other way pretty easily too; given a model, you can load the saved checkpoint into it like this (because from_flat_state accepts raw JAX Arrays in place of explicit Params): from safetensors.flax import load_file ... simple_dict = load_file("model.safetensors") dict_flat_state = {} for key, array in simple_dict.items(): elements = key.split(".") list_key = [] for element in elements: try: list_key.append(int(element)) except ValueError: list_key.append(element) dict_flat_state[tuple(list_key)] = array new_flat_state = nnx.from_flat_state(dict_flat_state) nnx.update(model, new_flat_state) A little more work than I'd ideally like, but given that it can be tucked away in general save_checkpoint/load_checkpoint functions, not too big a deal. Hope that's of use for other people coming across this problem! I'm beginning to feel a bit swamped with all of these libraries with names ending in -ax. It reminds me of the names of the characters in Asterix's village... ↩