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

Libraries

How to use indobenchmark/indogpt with Transformers:

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

pipe = pipeline("text-generation", model="indobenchmark/indogpt")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("indobenchmark/indogpt")
model = AutoModelForCausalLM.from_pretrained("indobenchmark/indogpt", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use indobenchmark/indogpt with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/indobenchmark/indogpt

SGLang

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

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

indogpt

Commit History

May 13, 2023

17:08
Adding `safetensors` variant of this model⁠

SFconvertbot

33163ef

Jun 21, 2022

17:51
Update tokenizer_config.json

samuelcahyawijaya

625917f
17:51
Update special_tokens_map.json

samuelcahyawijaya

aa76e50

Sep 27, 2021

18:05
update model & tokenizer meta

Samuel Cahyawijaya

f09b6f6
17:56
update model & tokenizer meta

Samuel Cahyawijaya

cdb45df

Sep 26, 2021

15:16
update README

Samuel Cahyawijaya

f810cd7
14:57
update indogpt model and tokenizer

Samuel Cahyawijaya

7f27ded

Sep 21, 2021

13:29
Create README.md

gentaiscool

525d88c
13:00
Upload IndoNLG_finals_indogpt_pytorch_model.bin with git-lfs

gentaiscool

7b56e67
12:57
Upload IndoNLG_finals_indogpt_config.json

gentaiscool

cb70303
12:57
Upload IndoNLG_finals_indogpt_tokenizer.model with git-lfs

gentaiscool

96ac66d
12:56
Upload IndoNLG_finals_indogpt_tokenizer.vocab

gentaiscool

6d0f416
12:55
initial commit

system HF Staff

540f144

Commit History

May 13, 2023

Adding `safetensors` variant of this model⁠

Jun 21, 2022

Update tokenizer_config.json

Update special_tokens_map.json

Sep 27, 2021

update model & tokenizer meta

update model & tokenizer meta

Sep 26, 2021

update README

update indogpt model and tokenizer

Sep 21, 2021

Create README.md

Upload IndoNLG_finals_indogpt_pytorch_model.bin with git-lfs

Upload IndoNLG_finals_indogpt_config.json

Upload IndoNLG_finals_indogpt_tokenizer.model with git-lfs

Upload IndoNLG_finals_indogpt_tokenizer.vocab

initial commit