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 / paper /fig_decay.png

vigneshwar234

Add figure fig_decay.png

e98f29a verified 12 days ago

download

history blame contribute delete

154 kB

Xet Pointer Details

( Raw pointer file )

Xet hash:: 7a8447d45c5a3a284e0821c3a5c6b1d9384435b283ad2ef5fe23d4fa49872d1a
Size of remote file:: 154 kB
SHA256:: 8139e8777f5665e1fd8e989dddf4bff7287e721c3ceacc0dd14373173b0c31a9

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.