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 20 hours ago

Commit

2531c36

verified ·

1 Parent(s): 836634c

Add pyproject.toml

Browse files

Files changed (1) hide show

pyproject.toml +36 -0

pyproject.toml ADDED Viewed

	@@ -0,0 +1,36 @@

+[build-system]
+requires = ["setuptools>=68", "wheel"]
+build-backend = "setuptools.backends.legacy:build"
+[project]
+name = "temporal-mesh-transformer"
+version = "0.1.0"
+description = "TemporalMesh Transformer — dynamic graph attention with temporal decay and adaptive depth routing"
+requires-python = ">=3.10"
+dependencies = [
+    "torch>=2.2.0",
+    "einops>=0.7.0",
+    "transformers>=4.40.0",
+    "datasets>=2.19.0",
+    "tokenizers>=0.19.0",
+    "wandb>=0.17.0",
+    "tensorboard>=2.16.0",
+    "matplotlib>=3.8.0",
+    "jupyter>=1.0.0",
+    "pandas>=2.2.0",
+    "tabulate>=0.9.0",
+]
+[project.optional-dependencies]
+dev = ["pytest>=8.0.0", "black>=24.0.0", "ruff>=0.4.0"]
+[tool.black]
+line-length = 100
+target-version = ["py310", "py311"]
+[tool.ruff]
+line-length = 100
+select = ["E", "F", "I"]
+[tool.pytest.ini_options]
+testpaths = ["tests"]