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 19 hours ago

Commit

836634c

verified ·

1 Parent(s): 5038ae1

Add requirements.txt

Browse files

Files changed (1) hide show

requirements.txt +27 -0

requirements.txt ADDED Viewed

	@@ -0,0 +1,27 @@

+# Core
+torch>=2.2.0
+torchvision
+torchaudio
+# TMT-specific
+einops>=0.7.0
+torch-geometric
+# NLP / data
+transformers>=4.40.0
+datasets>=2.19.0
+tokenizers>=0.19.0
+# Experiment tooling
+wandb>=0.17.0
+tensorboard>=2.16.0
+torchviz
+matplotlib>=3.8.0
+jupyter>=1.0.0
+pandas>=2.2.0
+tabulate>=0.9.0
+# Dev
+pytest>=8.0.0
+black>=24.0.0
+ruff>=0.4.0