Text Generation
Transformers
ONNX
TensorRT
English
text-generation-inference
causal-lm
int8
ENOT-AutoDL
Instructions to use ENOT-AutoDL/gpt2-tensorrt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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", device_map="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 Settings
- 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
| license: apache-2.0 | |
| datasets: | |
| - lambada | |
| language: | |
| - en | |
| library_name: transformers | |
| pipeline_tag: text-generation | |
| tags: | |
| - text-generation-inference | |
| - causal-lm | |
| - int8 | |
| - tensorrt | |
| - ENOT-AutoDL | |
| # GPT2 | |
| 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/) framework. | |
| Code for building of TensorRT engines and examples published on [github](https://github.com/ENOT-AutoDL/ENOT-transformers). | |
| ## Metrics: | |
| ### GPT2-XL | |
| | |TensorRT INT8+FP32|torch FP16| | |
| |---|:---:|:---:| | |
| | **Lambada Acc** |72.11%|71.43%| | |
| ### Test environment | |
| * GPU RTX 4090 | |
| * CPU 11th Gen Intel(R) Core(TM) i7-11700K | |
| * TensorRT 8.5.3.1 | |
| * pytorch 1.13.1+cu116 | |
| ## Latency: | |
| ### GPT2-XL | |
| |Input sequance length|Number of generated tokens|TensorRT INT8+FP32 ms|torch FP16 ms|Acceleration| | |
| |:---:|:---:|:---:|:---:|:---:| | |
| |64|64|462|1190|2.58| | |
| |64|128|920|2360|2.54| | |
| |64|256|1890|4710|2.54| | |
| ### Test environment | |
| * GPU RTX 4090 | |
| * CPU 11th Gen Intel(R) Core(TM) i7-11700K | |
| * TensorRT 8.5.3.1 | |
| * pytorch 1.13.1+cu116 | |
| ## How to use | |
| Example of inference and accuracy test [published on github](https://github.com/ENOT-AutoDL/ENOT-transformers): | |
| ```shell | |
| git clone https://github.com/ENOT-AutoDL/ENOT-transformers | |
| ``` | |