Instructions to use Chaeseung/dobae_tuned_KuLLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Chaeseung/dobae_tuned_KuLLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Chaeseung/dobae_tuned_KuLLM")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Chaeseung/dobae_tuned_KuLLM") model = AutoModelForCausalLM.from_pretrained("Chaeseung/dobae_tuned_KuLLM") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Chaeseung/dobae_tuned_KuLLM with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Chaeseung/dobae_tuned_KuLLM" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Chaeseung/dobae_tuned_KuLLM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Chaeseung/dobae_tuned_KuLLM
- SGLang
How to use Chaeseung/dobae_tuned_KuLLM 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 "Chaeseung/dobae_tuned_KuLLM" \ --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": "Chaeseung/dobae_tuned_KuLLM", "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 "Chaeseung/dobae_tuned_KuLLM" \ --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": "Chaeseung/dobae_tuned_KuLLM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Chaeseung/dobae_tuned_KuLLM with Docker Model Runner:
docker model run hf.co/Chaeseung/dobae_tuned_KuLLM
| { | |
| "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|>", | |
| "max_length": 4096, | |
| "model_max_length": 1000000000000000019884624838656, | |
| "pad_token": "<|unused0|>", | |
| "stride": 0, | |
| "tokenizer_class": "PreTrainedTokenizerFast", | |
| "truncation_side": "right", | |
| "truncation_strategy": "longest_first" | |
| } | |