Text Generation
Transformers
Safetensors
Korean
gpt_neox
polyglot-ko
gpt-neox
KoQuality
text-generation-inference
Instructions to use DILAB-HYU/koquality-polyglot-12.8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DILAB-HYU/koquality-polyglot-12.8b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="DILAB-HYU/koquality-polyglot-12.8b")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("DILAB-HYU/koquality-polyglot-12.8b") model = AutoModelForCausalLM.from_pretrained("DILAB-HYU/koquality-polyglot-12.8b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use DILAB-HYU/koquality-polyglot-12.8b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "DILAB-HYU/koquality-polyglot-12.8b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "DILAB-HYU/koquality-polyglot-12.8b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/DILAB-HYU/koquality-polyglot-12.8b
- SGLang
How to use DILAB-HYU/koquality-polyglot-12.8b 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 "DILAB-HYU/koquality-polyglot-12.8b" \ --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": "DILAB-HYU/koquality-polyglot-12.8b", "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 "DILAB-HYU/koquality-polyglot-12.8b" \ --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": "DILAB-HYU/koquality-polyglot-12.8b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use DILAB-HYU/koquality-polyglot-12.8b with Docker Model Runner:
docker model run hf.co/DILAB-HYU/koquality-polyglot-12.8b
Download tokenizer_config.json from DILAB-HYU/koquality-polyglot-12.8b: direct link, hf CLI and curl.
- Browser
- Download file 1.57 kB
-
https://huggingface.co/DILAB-HYU/koquality-polyglot-12.8b/resolve/cd5a8af6760357b7711263cd3dcbe59652df712a/tokenizer_config.json
- Command line
-
hf download hf://DILAB-HYU/koquality-polyglot-12.8b@cd5a8af6760357b7711263cd3dcbe59652df712a/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/DILAB-HYU/koquality-polyglot-12.8b/resolve/cd5a8af6760357b7711263cd3dcbe59652df712a/tokenizer_config.json
1.57 kB
| { | |
| "added_tokens_decoder": { | |
| "0": { | |
| "content": "<|unused0|>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false, | |
| "special": true | |
| }, | |
| "1": { | |
| "content": "<|unused1|>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false, | |
| "special": true | |
| }, | |
| "2": { | |
| "content": "<|endoftext|>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false, | |
| "special": true | |
| }, | |
| "3": { | |
| "content": "<|sep|>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false, | |
| "special": true | |
| }, | |
| "30000": { | |
| "content": "<|acc|>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false, | |
| "special": true | |
| }, | |
| "30001": { | |
| "content": "<|tel|>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false, | |
| "special": true | |
| }, | |
| "30002": { | |
| "content": "<|rrn|>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false, | |
| "special": true | |
| } | |
| }, | |
| "additional_special_tokens": [ | |
| "<|endoftext|>", | |
| "<|sep|>", | |
| "<|acc|>", | |
| "<|tel|>", | |
| "<|rrn|>" | |
| ], | |
| "clean_up_tokenization_spaces": true, | |
| "eos_token": "<|endoftext|>", | |
| "model_max_length": 1000000000000000019884624838656, | |
| "pad_token": "<|endoftext|>", | |
| "tokenizer_class": "PreTrainedTokenizerFast" | |
| } | |