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 18 hours ago

Commit

5732644

verified ·

1 Parent(s): 7078228

Add DOI, expanded tags, full benchmark metrics to model card YAML

Browse files

Files changed (1) hide show

README.md +33 -0

README.md CHANGED Viewed

@@ -17,12 +17,21 @@ tags:
 - causal-lm
 - research
 - preprint
 library_name: pytorch
 pipeline_tag: text-generation
 datasets:
 - vigneshwar234/TMT-Benchmarks
 metrics:
 - perplexity
 model-index:
 - name: TemporalMesh Transformer (TMT-Base)
   results:
@@ -32,11 +41,35 @@ model-index:
     dataset:
       type: wikitext
       name: WikiText-2
     metrics:
     - type: perplexity
       value: 29.4
       name: Validation Perplexity
       verified: false
 ---
 <div align="center">

 - causal-lm
 - research
 - preprint
+- mesh-transformer
+- dynamic-graph
+- early-exit
+- per-token-routing
 library_name: pytorch
 pipeline_tag: text-generation
 datasets:
 - vigneshwar234/TMT-Benchmarks
 metrics:
 - perplexity
+doi: 10.5281/zenodo.20287390
+extra_gated_prompt: |
+  Paper DOI: https://doi.org/10.5281/zenodo.20287390
+  Zenodo: https://zenodo.org/records/20287390
+  GitHub: https://github.com/vignesh2027/TemporalMesh-Transformer
 model-index:
 - name: TemporalMesh Transformer (TMT-Base)
   results:
     dataset:
       type: wikitext
       name: WikiText-2
+      config: wikitext-2-raw-v1
+      split: validation
     metrics:
     - type: perplexity
       value: 29.4
       name: Validation Perplexity
       verified: false
+  - task:
+      type: text-generation
+      name: Efficient Inference
+    dataset:
+      type: wikitext
+      name: WikiText-2
+      config: wikitext-2-raw-v1
+      split: validation
+    metrics:
+    - type: perplexity
+      value: 29.4
+      name: Validation Perplexity
+      verified: false
+    - name: Relative Compute
+      type: efficiency
+      value: 0.48
+      verified: false
+    - name: Avg Exit Layer
+      type: efficiency
+      value: 5.5
+      verified: false
+---
 ---
 <div align="center">