Instructions to use qbz506/psalm-arm-d with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use qbz506/psalm-arm-d with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="qbz506/psalm-arm-d", trust_remote_code=True)# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("qbz506/psalm-arm-d", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
PSALM ELC-PSALM-S — arm D (Paribhāṣā dose)
Small bidirectional ELC-BERT-style encoder trained from scratch under the BabyLM Strict-Small protocol. This is ablation arm D: the stage-one structural dose is Paribhāṣā, trimmed to the same token budget as every other arm over a shared English base, so differences between arms are attributable to dose content under a fixed budget rather than to data volume.
Trained jointly with masked and causal objectives; minimal pairs are scored by
Salazar-style pseudo-log-likelihood. The export registers both AutoModel (base
encoder, returns last_hidden_state) and AutoModelForMaskedLM, so the official
BabyLM (Super)GLUE fine-tuner can load it directly.
from transformers import AutoModelForMaskedLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("qbz506/psalm-arm-d", trust_remote_code=True)
model = AutoModelForMaskedLM.from_pretrained("qbz506/psalm-arm-d", trust_remote_code=True)
See the project site and
repository for the method, the seed-replicated
results, and the scope statement. This checkpoint is part of a controlled scientific
ablation; for the leaderboard-track model see qbz506/psalm-submission.
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