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epfl-dlab
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Model Details
Model Provider
epfl-dlab
Model Tree
Input Modalities
Output Modalities
Supported Functionality
Intermediate checkpoints are published as git revisions on this repo, so any point in the trajectory can be loaded by passing revision=:
python
from transformers import AutoModelForCausalLM, AutoTokenizerimport torchrepo = "epfl-dlab/spp-vanilla-3b-base"tok = AutoTokenizer.from_pretrained(repo) # identical at every revisionmodel = AutoModelForCausalLM.from_pretrained(repo, revision="step-25000", dtype=torch.bfloat16, device_map="auto")
| Revision | Pretraining step | Tokens seen | LR phase |
|---|---|---|---|
step-25000 | 25,000 / 254,313 | ~49.2B | stable |
step-50000 | 50,000 / 254,313 | ~98.3B | stable |
step-75000 | 75,000 / 254,313 | ~147B | stable |
step-100000 | 100,000 / 254,313 | ~197B | stable |
step-125000 | 125,000 / 254,313 | ~246B | stable |
step-150000 | 150,000 / 254,313 | ~295B | stable |
step-175000 | 175,000 / 254,313 | ~344B | stable |
step-200000 | 200,000 / 254,313 | ~393B | stable |
step-225000 | 225,000 / 254,313 | ~442B | stable |
step-240000 | 240,000 / 254,313 | ~472B | linear decay |
step-254313 | 254,313 / 254,313 | ~500B | linear decay — same weights as main |
main always holds the finished model (step 254,313).
Only model weights are published — optimizer and RNG state are not included, so these revisions support evaluation, probing, and fine-tuning, but not exact resumption of the original run.
Research on alignment and safety. As a base model it is meant for continuation, probing, or further fine-tuning; it is not instruction-tuned and can produce incorrect or unsafe content.
License: to be finalised.