Instructions to use thisisHJLee/polyglot_ko_newssample_03 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use thisisHJLee/polyglot_ko_newssample_03 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="thisisHJLee/polyglot_ko_newssample_03")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("thisisHJLee/polyglot_ko_newssample_03") model = AutoModelForMultimodalLM.from_pretrained("thisisHJLee/polyglot_ko_newssample_03") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use thisisHJLee/polyglot_ko_newssample_03 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "thisisHJLee/polyglot_ko_newssample_03" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "thisisHJLee/polyglot_ko_newssample_03", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/thisisHJLee/polyglot_ko_newssample_03
- SGLang
How to use thisisHJLee/polyglot_ko_newssample_03 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 "thisisHJLee/polyglot_ko_newssample_03" \ --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": "thisisHJLee/polyglot_ko_newssample_03", "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 "thisisHJLee/polyglot_ko_newssample_03" \ --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": "thisisHJLee/polyglot_ko_newssample_03", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use thisisHJLee/polyglot_ko_newssample_03 with Docker Model Runner:
docker model run hf.co/thisisHJLee/polyglot_ko_newssample_03
Commit ·
b0c4f2f
1
Parent(s): 8f52a6b
Upload tokenizer
Browse files- special_tokens_map.json +12 -0
- tokenizer.json +0 -0
- tokenizer_config.json +9 -0
special_tokens_map.json
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{
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"additional_special_tokens": [
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"<|endoftext|>",
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"<|sep|>",
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"<|acc|>",
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"<|tel|>",
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"<|rrn|>"
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],
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"bos_token": "<|startoftext|>",
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"eos_token": "<|endoftext|>",
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"pad_token": "<|pad|>"
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}
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tokenizer_config.json
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{
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"bos_token": "<|startoftext|>",
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"clean_up_tokenization_spaces": true,
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"eos_token": "<|endoftext|>",
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"model_max_length": 1000000000000000019884624838656,
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"pad_token": "<|pad|>",
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"special_tokens_map_file": "C:\\Users\\hj.lee/.cache\\huggingface\\hub\\models--EleutherAI--polyglot-ko-1.3b\\snapshots\\711da2957fdae31202de86c51edbc0c7f433b9e5\\special_tokens_map.json",
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"tokenizer_class": "PreTrainedTokenizerFast"
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}
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