Transformers
PyTorch
English
language-model
graph-attention
adaptive-depth
temporal-decay
efficient-llm
Eval Results (legacy)
Instructions to use vigneshwar234/TemporalMesh-Transformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vigneshwar234/TemporalMesh-Transformer with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("vigneshwar234/TemporalMesh-Transformer", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| results: | |
| - task: | |
| type: Language Modelling | |
| name: Language Modelling | |
| dataset: | |
| name: WikiText-2 | |
| type: wikitext-2 | |
| config: wikitext-2-raw-v1 | |
| split: test | |
| metrics: | |
| - type: Perplexity | |
| value: 29.4 | |
| name: Perplexity | |
| verified: false | |
| - task: | |
| type: Language Modelling | |
| name: Language Modelling | |
| dataset: | |
| name: WikiText-103 | |
| type: wikitext-103 | |
| config: wikitext-103-raw-v1 | |
| split: test | |
| metrics: | |
| - type: Perplexity | |
| value: 36.1 | |
| name: Perplexity | |
| verified: false | |
| - task: | |
| type: Long-context Language Modelling | |
| name: Long-context LM | |
| dataset: | |
| name: LongBench | |
| type: longbench | |
| split: test | |
| metrics: | |
| - type: Average Score | |
| value: 53.4 | |
| name: Average Score | |
| verified: false | |