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 17 hours ago

Commit

2138d01

verified ·

1 Parent(s): 97ade79

Add CITATION.cff

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+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."