Downloads · 30 days
0
pythia-160m-ppt-control_nca-seed324
sashaboguraev/pythia-160m-ppt-control_nca-seed324
pythia-160m-ppt-control_nca-seed324 is a machine learning model from sashaboguraev. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
Trained from scratch using the ppt pre-pretraining research framework.
Access
Public
Updated May 12, 2026
Repo size
7.9 GB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.pt5.3 GB · 62%
At a glance
- License
- mit
- Access
- Public
- Created
- May 12, 2026
- Updated
- May 12, 2026
- SHA
- 2b79cf8b
Base models
Model card
From the Hugging Face model README
Pythia-160M Pre-Pretraining: control_nca (seed 324)
Trained from scratch using the ppt pre-pretraining research framework.
Training Details
| Parameter | Value |
|---|---|
| Base architecture | EleutherAI/pythia-160m (reinitialized) |
| Regimen | control_nca |
| Seed | 324 |
| Stage 1 dataset | Shuffled NCA tokens (unstructured control) |
| Stage 1 steps | 5000 |
| Stage 2 dataset | OpenWebText |
| Stage 2 steps | 10000 |
| Optimizer | AdamW (lr=1e-3, wd=0.0) |
| Effective batch size | 64 |
| Sequence length | 2048 |
Control Design
Stage 1: NCA tokens with shuffled order. Same token distribution as nca but no sequential structure. This is the unstructured control.
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("sashaboguraev/pythia-160m-ppt-control_nca-seed324")
tokenizer = AutoTokenizer.from_pretrained("sashaboguraev/pythia-160m-ppt-control_nca-seed324")
Citation
If you use this model, please cite the original pre-pretraining papers:
- Papadimitriou & Jurafsky (2020) — tilt-transfer
- Hahn & Rofin (2024) — pre-pretraining with formal languages (michahu)
- Lee et al. (2024) — NCA pre-pretraining (danihyunlee)
- License
- mit
- Languages
- en
- Created
- May 12, 2026
- Updated
- May 12, 2026