gslm-scaling-20m-87p1b

Model-only FP32 weights for a final-budget scaling-curve endpoint from Scaling Properties of Speech Language Models (EMNLP 2024). This checkpoint has 20,710,912 parameters and saw exactly 87,097,120,000 tokens at 131,200 tokens per update.

import torch
from slm import TransformerLM

model = TransformerLM.from_pretrained("tiagoCuervo/gslm-scaling-20m-87p1b", device="cuda")
prompt = torch.tensor([[12, 91, 204]], device="cuda")
units = model.generate(prompt, max_new_tokens=200, temperature=0.8, top_k=50)

Evaluation scores are 57.15% sBLIMP phenomenon macro accuracy, 54.33% sBLIMP voice-pair accuracy, 53.34% sStoryCloze, and 70.55% tStoryCloze.

The model predicts consecutive-run-collapsed 25 Hz layer-11 mHuBERT K-means units (K=500; EOS=500; PAD=501). A vocoder is not bundled.

Speech-tokenizer and vocoder artifacts are credited in the SLM third-party notices. See provenance.json for exact hashes and evaluation metadata.