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tiagoCuervo/
gslm-scaling-155m-4p9b

Name: gslm-scaling-155m-4p9b
Availability: InStock

slm-research

safetensors

slm

speech-language-model

audio

text-generation

apache-2.0

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gslm-scaling-155m-4p9b

Model-only FP32 weights for a final-budget scaling-curve endpoint from Scaling Properties of Speech Language Models (EMNLP 2024). This checkpoint has 154,680,320 parameters and saw exactly 4,933,120,000 tokens at 524,800 tokens per update.

python

import torch
from slm import TransformerLM

model = TransformerLM.from_pretrained("tiagoCuervo/gslm-scaling-155m-4p9b", 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 57.32% sBLIMP phenomenon macro accuracy, 54.59% sBLIMP voice-pair accuracy, 53.71% sStoryCloze, and 70.76% 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.