Text Generation
Transformers
PyTorch
Indonesian
gpt2
indogpt
indobenchmark
indonlg
text-generation-inference
Instructions to use indobenchmark/indogpt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("indobenchmark/indogpt") model = AutoModelForCausalLM.from_pretrained("indobenchmark/indogpt") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- 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
Samuel Cahyawijaya commited on
Commit ·
7f27ded
1
Parent(s): 525d88c
update indogpt model and tokenizer
Browse files
IndoNLG_finals_indogpt_tokenizer.vocab
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IndoNLG_finals_indogpt_config.json → config.json
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"max_length": 50
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"vocab_size": 40005
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}
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"max_length": 50
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}
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"tokenizer_class": "IndoNLGTokenizer",
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"vocab_size": 40005
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}
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IndoNLG_finals_indogpt_pytorch_model.bin → pytorch_model.bin
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IndoNLG_finals_indogpt_tokenizer.model → sentencepiece.bpe.model
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special_tokens_map.json
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{"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "sep_token": "</s>", "pad_token": "<pad>", "cls_token": "<s>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true}, "additional_special_tokens": ["[java]", "[sunda]", "[indo]", "<mask>"]}
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tokenizer_config.json
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{"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "sep_token": "</s>", "cls_token": "<s>", "pad_token": "<pad>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "additional_special_tokens": ["[java]", "[sunda]", "[indo]", "<mask>"]}
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