Instructions to use ENOT-AutoDL/gpt2-tensorrt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ENOT-AutoDL/gpt2-tensorrt with Transformers:

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

pipe = pipeline("text-generation", model="ENOT-AutoDL/gpt2-tensorrt")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ENOT-AutoDL/gpt2-tensorrt", dtype="auto")

TensorRT

How to use ENOT-AutoDL/gpt2-tensorrt with TensorRT:

# No code snippets available yet for this library.

# To use this model, check the repository files and the library's documentation.

# Want to help? PRs adding snippets are welcome at:
# https://github.com/huggingface/huggingface.js

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ENOT-AutoDL/gpt2-tensorrt with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ENOT-AutoDL/gpt2-tensorrt"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ENOT-AutoDL/gpt2-tensorrt",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ENOT-AutoDL/gpt2-tensorrt

SGLang

How to use ENOT-AutoDL/gpt2-tensorrt 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 "ENOT-AutoDL/gpt2-tensorrt" \
    --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": "ENOT-AutoDL/gpt2-tensorrt",
		"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 "ENOT-AutoDL/gpt2-tensorrt" \
        --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": "ENOT-AutoDL/gpt2-tensorrt",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ENOT-AutoDL/gpt2-tensorrt with Docker Model Runner:
```
docker model run hf.co/ENOT-AutoDL/gpt2-tensorrt
```

igor commited on Jun 8, 2023

Commit

84e78ed

1 Parent(s): 918550e

fixed typo

Browse files

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -20,7 +20,7 @@ This repository contains GPT2 onnx models compatible with TensorRT:
 * gpt2-xl.onnx - GPT2-XL onnx for fp32 or fp16 engines
 * gpt2-xl-i8.onnx - GPT2-XL onnx for int8+fp32 engines
-Quantization of models was performed by the [ENOT-AutoDL](https://pypi.org/project/enot-autodl/) framewor.
 Code for building of TensorRT engines and examples published on [github](https://github.com/ENOT-AutoDL/ENOT-transformers).
 ## Metrics:

 * gpt2-xl.onnx - GPT2-XL onnx for fp32 or fp16 engines
 * gpt2-xl-i8.onnx - GPT2-XL onnx for int8+fp32 engines
+Quantization of models was performed by the [ENOT-AutoDL](https://pypi.org/project/enot-autodl/) framework.
 Code for building of TensorRT engines and examples published on [github](https://github.com/ENOT-AutoDL/ENOT-transformers).
 ## Metrics: