Instructions to use ENOT-AutoDL/gpt-j-6B-tensorrt-int8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ENOT-AutoDL/gpt-j-6B-tensorrt-int8 with Transformers:

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

pipe = pipeline("text-generation", model="ENOT-AutoDL/gpt-j-6B-tensorrt-int8")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ENOT-AutoDL/gpt-j-6B-tensorrt-int8", dtype="auto")

TensorRT

How to use ENOT-AutoDL/gpt-j-6B-tensorrt-int8 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/gpt-j-6B-tensorrt-int8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ENOT-AutoDL/gpt-j-6B-tensorrt-int8"
# 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/gpt-j-6B-tensorrt-int8",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ENOT-AutoDL/gpt-j-6B-tensorrt-int8

SGLang

How to use ENOT-AutoDL/gpt-j-6B-tensorrt-int8 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/gpt-j-6B-tensorrt-int8" \
    --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/gpt-j-6B-tensorrt-int8",
		"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/gpt-j-6B-tensorrt-int8" \
        --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/gpt-j-6B-tensorrt-int8",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ENOT-AutoDL/gpt-j-6B-tensorrt-int8 with Docker Model Runner:
```
docker model run hf.co/ENOT-AutoDL/gpt-j-6B-tensorrt-int8
```

INT8 GPT-J 6B

GPT-J 6B is a transformer model trained using Ben Wang's Mesh Transformer JAX. "GPT-J" refers to the class of model, while "6B" represents the number of trainable parameters.

This repository contains GPT-J 6B onnx model suitable for building TensorRT int8+fp32 engines. Quantization of model was performed by the ENOT-AutoDL framework. Code for building of TensorRT engines and examples published on github.

Metrics:

	TensorRT INT8+FP32	torch FP16	torch FP32
Lambada Acc	78.46%	79.53%	-
Model size (GB)	8.5	12.1	24.2

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:

Input sequance length	Number of generated tokens	TensorRT INT8+FP32 ms	torch FP16 ms	Acceleration
64	64	1040	1610	1.55
64	128	2089	3224	1.54
64	256	4236	6479	1.53
128	64	1060	1619	1.53
128	128	2120	3241	1.53
128	256	4296	6510	1.52
256	64	1109	1640	1.49
256	128	2204	3276	1.49
256	256	4443	6571	1.49

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:

git clone https://github.com/ENOT-AutoDL/ENOT-transformers

Downloads last month: -; Downloads are not tracked for this model. How to track

ENOT-AutoDL
/

gpt-j-6B-tensorrt-int8

INT8 GPT-J 6B

Metrics:

Test environment

Latency:

Test environment

How to use

Dataset used to train ENOT-AutoDL/gpt-j-6B-tensorrt-int8