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 6 days ago

Commit

05b3a30

verified ·

1 Parent(s): 9bb9f71

Add live demo Space badge + section to model card

Browse files

Files changed (1) hide show

README.md +11 -0

README.md CHANGED Viewed

@@ -79,6 +79,7 @@ model-index:
 ### *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)
@@ -230,6 +231,16 @@ k_m        ←  0.99 · k_m + 0.01 · mean(attending tokens)
 ---
 ## Quick Start
 ### Installation

 ### *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)
+[![Space](https://img.shields.io/badge/🤗%20Space-Live%20Demo-orange?style=flat-square)](https://huggingface.co/spaces/vigneshwar234/TemporalMesh-Transformer-Demo)
 [![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)
 ---
+## 🚀 Live Demo
+Try TMT interactively — no install needed:
+👉 **[huggingface.co/spaces/vigneshwar234/TemporalMesh-Transformer-Demo](https://huggingface.co/spaces/vigneshwar234/TemporalMesh-Transformer-Demo)**
+Visualise exit gates, dynamic attention graphs, and per-token compute depth on any sentence you type.
+---
 ## Quick Start
 ### Installation