Organization

Pastel Labs

pastel-labs

Edge inference, Private / TEE inference, In-browser / On-phone inference, embeddings, search

Models in Library3

Datasets in Library0

Models on Hugging Face4

Followers2

Compare the published evidence

This profile connects the 3 model and dataset records shown below to their upstream cards and sources. The library counts describe SAVRN's coverage, rather than this publisher's entire output.

Published tasks: Feature extraction, Fill mask.

Reported licenses: apache-2.0. Open a record to review its specific license and conditions.

Compare model specifications, source dates, and hardware estimates on the individual records. Publisher identity verification is separate from a model's quality, deployment readiness, or suitability for your work.

Models

Model · Fill mask

UltraBERT-380M-ONNX

Pastel Labs

ONNX export of dmis-lab/UltraBERT-380M for ONNX Runtime and Transformers.js. All credit for the model goes to dmis-lab. The weights are theirs, under the same Apache-2.0 license. We only converted the format. Inputs are inputids and attentionmask, with dynamic batch size and sequence length. Output is lasthiddenstate plus logits from the masked-language-model head. The sliding-window (128 tokens) and full-attention layers are built from the attention mask inside the graph, so padding and any length work. We ran 20 English texts through the original PyTorch model and through each ONNX file, then compared mean-pooled last hidden states with cosine similarity. Fifteen texts are short (3 to 25…

Open weights apache-2.0 transformers.js

View model

Model · Fill mask

UltraBERT-150M-ONNX

Pastel Labs

ONNX export of dmis-lab/UltraBERT-150M for ONNX Runtime and Transformers.js. All credit for the model goes to dmis-lab. The weights are theirs, under the same Apache-2.0 license. We only converted the format. Inputs are inputids and attentionmask, with dynamic batch size and sequence length. Output is lasthiddenstate plus logits from the masked-language-model head. The sliding-window (128 tokens) and full-attention layers are built from the attention mask inside the graph, so padding and any length work. We ran 20 English texts through the original PyTorch model and through each ONNX file, then compared mean-pooled last hidden states with cosine similarity. Fifteen texts are short (3 to 25…

Open weights apache-2.0 transformers.js

View model

Model · Feature extraction

UltraBERT-Embed-150M-ONNX

Pastel Labs

ONNX export of dmis-lab/UltraBERT-Embed-150M for ONNX Runtime and Transformers.js. All credit for the model goes to dmis-lab. The weights are theirs, under the same Apache-2.0 license. We only converted the format. Inputs are inputids and attentionmask, with dynamic batch size and sequence length. Output is lasthiddenstate. The sliding-window (128 tokens) and full-attention layers are built from the attention mask inside the graph, so padding and any length work. We ran 20 English texts through the original PyTorch model and through each ONNX file, then compared the sentence embedding (first-token pooling, as in the sentence-transformers config) with cosine similarity. Fifteen texts are…

Open weights apache-2.0 transformers.js

View model