Instructions to use Angshul/SpliNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Angshul/SpliNet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Angshul/SpliNet", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("Angshul/SpliNet", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Angshul/SpliNet: direct link, hf CLI and curl.
- Browser
- Download file 1.11 kB
-
https://huggingface.co/Angshul/SpliNet/resolve/main/README.md
- Command line
-
hf download hf://Angshul/SpliNet/README.md
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curl -L -o README.md https://huggingface.co/Angshul/SpliNet/resolve/main/README.md
1.11 kB
metadata
language:
- en
pipeline_tag: fill-mask
library_name: transformers
datasets:
- allenai/c4
SpliNet
SpliNet: A Zero-Parameter B-Spline Transformer with Linear Complexity
SpliNet replaces learned self-attention token mixing with a fixed order-2 single-sided cardinal B-spline operator.
This model was pretrained from scratch on exactly 2,000,000,000 C4 tokens using the dedicated SpliNet tokenizer.
Architecture
- Layers: 12
- Hidden size: 768
- Heads: 12
- FFN width: 3072
- Sequence length: 512
- Vocabulary: 32000
- Spline order: 2
- Spline radius: 16
- Trainable mixer parameters: 0
- Total parameters: 82,894,592
- Trainable parameters: 82,894,592
Pretraining
- Training tokens: 2,000,000,000
- Validation tokens: 5,120,000
- Objective: masked language modeling
- Masked positions: 77/512
- Optimizer: AdamW
- Precision: BF16
- Hardware: NVIDIA A100-SXM4-80GB
Final validation
- MLM loss: 4.614502
- MLM perplexity: 100.937575
Load with trust_remote_code=True.
OpenReview: https://openreview.net/forum?id=nWHnuiEF3C GitHub: https://github.com/AngshulMajumdar/SpliNet