Text Generation
PyTorch
Transformers
English
language-model
graph-neural-network
sparse-attention
adaptive-depth
temporal-decay
mesh-attention
efficient-transformer
novel-architecture
causal-lm
research
preprint
mesh-transformer
dynamic-graph
early-exit
per-token-routing
Eval Results (legacy)
Instructions to use vigneshwar234/TemporalMesh-Transformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
File size: 851 Bytes
2531c36 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 | [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"]
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