Instructions to use vigneshwar234/TemporalMesh-Transformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vigneshwar234/TemporalMesh-Transformer with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="vigneshwar234/TemporalMesh-Transformer")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("vigneshwar234/TemporalMesh-Transformer", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use vigneshwar234/TemporalMesh-Transformer with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vigneshwar234/TemporalMesh-Transformer"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vigneshwar234/TemporalMesh-Transformer",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/vigneshwar234/TemporalMesh-Transformer

SGLang

How to use vigneshwar234/TemporalMesh-Transformer with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "vigneshwar234/TemporalMesh-Transformer" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vigneshwar234/TemporalMesh-Transformer",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "vigneshwar234/TemporalMesh-Transformer" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vigneshwar234/TemporalMesh-Transformer",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use vigneshwar234/TemporalMesh-Transformer with Docker Model Runner:
```
docker model run hf.co/vigneshwar234/TemporalMesh-Transformer
```

vigneshwar234 commited on about 14 hours ago

Commit

9bb9f71

verified ·

1 Parent(s): 5732644

Add Zenodo DOI badge, update citation with DOI, link paper badge to Zenodo

Browse files

Files changed (1) hide show

README.md +8 -3

README.md CHANGED Viewed

@@ -78,10 +78,12 @@ model-index:
 ### *Dynamic Graph Attention · Temporal Semantic Decay · Per-Token Adaptive Depth Routing*
 [![GitHub](https://img.shields.io/badge/GitHub-vignesh2027%2FTemporalMesh--Transformer-181717?style=flat-square&logo=github)](https://github.com/vignesh2027/TemporalMesh-Transformer)
-[![Paper PDF](https://img.shields.io/badge/Paper-PDF%2020%20pages-red?style=flat-square&logo=adobeacrobatreader)](https://huggingface.co/vigneshwar234/TemporalMesh-Transformer/resolve/main/paper/TemporalMesh_Transformer_2026.pdf)
 [![Dataset](https://img.shields.io/badge/Dataset-TMT--Benchmarks-FFD21E?style=flat-square&logo=huggingface)](https://huggingface.co/datasets/vigneshwar234/TMT-Benchmarks)
 [![License: MIT](https://img.shields.io/badge/License-MIT-green?style=flat-square)](https://github.com/vignesh2027/TemporalMesh-Transformer/blob/main/LICENSE)
 **Val. Perplexity: 29.4** · **~50% compute reduction** · **~120M parameters** · **WikiText-2**
@@ -432,9 +434,12 @@ print(ds['test'][0])
                   Temporal Decay and Adaptive Depth Routing},
   author       = {Vignesh},
   year         = {2026},
-  url          = {https://huggingface.co/vigneshwar234/TemporalMesh-Transformer},
   note         = {Preprint. Novel architecture combining mesh attention, temporal
-                  decay encoding, and per-token adaptive depth routing.}
 }
 ```

 ### *Dynamic Graph Attention · Temporal Semantic Decay · Per-Token Adaptive Depth Routing*
+[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.20287390.svg)](https://doi.org/10.5281/zenodo.20287390)
 [![GitHub](https://img.shields.io/badge/GitHub-vignesh2027%2FTemporalMesh--Transformer-181717?style=flat-square&logo=github)](https://github.com/vignesh2027/TemporalMesh-Transformer)
+[![Paper PDF](https://img.shields.io/badge/Paper-PDF%2020%20pages-red?style=flat-square&logo=adobeacrobatreader)](https://doi.org/10.5281/zenodo.20287390)
 [![Dataset](https://img.shields.io/badge/Dataset-TMT--Benchmarks-FFD21E?style=flat-square&logo=huggingface)](https://huggingface.co/datasets/vigneshwar234/TMT-Benchmarks)
 [![License: MIT](https://img.shields.io/badge/License-MIT-green?style=flat-square)](https://github.com/vignesh2027/TemporalMesh-Transformer/blob/main/LICENSE)
+[![Zenodo](https://img.shields.io/badge/Zenodo-Published-blue?style=flat-square&logo=zenodo)](https://zenodo.org/records/20287390)
 **Val. Perplexity: 29.4** · **~50% compute reduction** · **~120M parameters** · **WikiText-2**
                   Temporal Decay and Adaptive Depth Routing},
   author       = {Vignesh},
   year         = {2026},
+  doi          = {10.5281/zenodo.20287390},
+  url          = {https://doi.org/10.5281/zenodo.20287390},
+  publisher    = {Zenodo},
   note         = {Preprint. Novel architecture combining mesh attention, temporal
+                  decay encoding, and per-token adaptive depth routing.
+                  Code: https://github.com/vignesh2027/TemporalMesh-Transformer}
 }
 ```