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
```

TemporalMesh-Transformer

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2138d01

cff-version: 1.2.0
message: "If you use TMT in your research, please cite it as below."
type: software
title: "TemporalMesh Transformer (TMT)"
abstract: >
  A novel autoregressive language model architecture that simultaneously fuses
  dynamic graph topology (Mesh Attention), token-level temporal semantic decay,
  and per-token adaptive depth routing into a single unified model.
  Achieves ~50% compute reduction and lower perplexity vs. parameter-matched baselines.
authors:
  - name: "Vignesh"
    alias: "vignesh2027"
repository-code: "https://github.com/vignesh2027/TemporalMesh-Transformer"
url: "https://huggingface.co/vigneshwar234/TemporalMesh-Transformer"
license: MIT
version: "1.0.0"
date-released: "2026-05-19"
keywords:
  - transformer
  - mesh-attention
  - temporal-decay
  - adaptive-depth
  - graph-neural-network
  - efficient-transformer
  - language-model
  - PyTorch
  - NLP
  - deep-learning
preferred-citation:
  type: generic
  title: >
    TemporalMesh Transformer: Dynamic Graph Attention with Temporal Decay
    and Adaptive Depth Routing
  authors:
    - name: "Vignesh"
  year: 2026
  url: "https://huggingface.co/vigneshwar234/TemporalMesh-Transformer"
  notes: "Preprint. Available at GitHub and Hugging Face."