Instructions to use martymukherjee/ControlLyapunovTransformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use martymukherjee/ControlLyapunovTransformer with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="martymukherjee/ControlLyapunovTransformer")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("martymukherjee/ControlLyapunovTransformer", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use martymukherjee/ControlLyapunovTransformer with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "martymukherjee/ControlLyapunovTransformer"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "martymukherjee/ControlLyapunovTransformer",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/martymukherjee/ControlLyapunovTransformer

SGLang

How to use martymukherjee/ControlLyapunovTransformer 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 "martymukherjee/ControlLyapunovTransformer" \
    --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": "martymukherjee/ControlLyapunovTransformer",
		"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 "martymukherjee/ControlLyapunovTransformer" \
        --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": "martymukherjee/ControlLyapunovTransformer",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use martymukherjee/ControlLyapunovTransformer with Docker Model Runner:
```
docker model run hf.co/martymukherjee/ControlLyapunovTransformer
```

martymukherjee commited on Apr 6, 2025

Commit

c8cfbb6

verified ·

1 Parent(s): 72294a2

Uploading encoder-decoder model

Browse files

Files changed (2) hide show

decoder_mask.pth +1 -1
encoder_mask.pth +1 -1

decoder_mask.pth CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:ed1307337589317c2c7e3bd83efe300990d399b28c7ea55a97582571fc36ed2c
 size 170822057

 version https://git-lfs.github.com/spec/v1
+oid sha256:4ae4dcd823e62822af9d99e49cb059951226cc553f6bc4df9bfb2b75ed69e974
 size 170822057

encoder_mask.pth CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:5feef68228606c480441938634d3ea015cfded666cae6a6a2485f03f861961ec
 size 131382770

 version https://git-lfs.github.com/spec/v1
+oid sha256:89461bfff082808a451a87245eb34432f1f65db5f795596d4f507c0c66a708da
 size 131382770