Text Generation
Transformers
Safetensors
Korean
llama
korean
causal-lm
instruction-tuned
from-scratch
kawk
conversational
text-generation-inference
Instructions to use Infinity08/KAWK-500M-Korean-Instruct-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Infinity08/KAWK-500M-Korean-Instruct-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Infinity08/KAWK-500M-Korean-Instruct-v1") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Infinity08/KAWK-500M-Korean-Instruct-v1") model = AutoModelForCausalLM.from_pretrained("Infinity08/KAWK-500M-Korean-Instruct-v1", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Infinity08/KAWK-500M-Korean-Instruct-v1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Infinity08/KAWK-500M-Korean-Instruct-v1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Infinity08/KAWK-500M-Korean-Instruct-v1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Infinity08/KAWK-500M-Korean-Instruct-v1
- SGLang
How to use Infinity08/KAWK-500M-Korean-Instruct-v1 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 "Infinity08/KAWK-500M-Korean-Instruct-v1" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Infinity08/KAWK-500M-Korean-Instruct-v1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "Infinity08/KAWK-500M-Korean-Instruct-v1" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Infinity08/KAWK-500M-Korean-Instruct-v1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Infinity08/KAWK-500M-Korean-Instruct-v1 with Docker Model Runner:
docker model run hf.co/Infinity08/KAWK-500M-Korean-Instruct-v1
| { | |
| "run_id": "20260809T050630Z", | |
| "started_at_utc": "2026-08-09T05:06:30.405208+00:00", | |
| "tasks": [ | |
| "kobest", | |
| "kmmlu" | |
| ], | |
| "num_fewshot": 0, | |
| "limit": null, | |
| "device": "cuda:0", | |
| "dtype": "bfloat16", | |
| "batch_size": 64, | |
| "max_length": 1024, | |
| "bootstrap_iters": 1000, | |
| "seed": 1234, | |
| "models": [ | |
| { | |
| "name": "kawk500m-instruct-v1", | |
| "checkpoint": "artifacts\\runpod-checkpoints\\kawk500m-korean-instruct-v1\\checkpoint-00000739", | |
| "model_dir": "C:\\Users\\wonye\\project\\AI\\KAWK50M\\artifacts\\runpod-checkpoints\\kawk500m-korean-instruct-v1\\checkpoint-00000739\\model", | |
| "tokenizer_dir": "C:\\Users\\wonye\\project\\AI\\KAWK50M\\artifacts\\runpod-checkpoints\\kawk500m-korean-instruct-v1\\checkpoint-00000739\\tokenizer", | |
| "elapsed_seconds": 907.9745282999938, | |
| "result_path": "C:\\Users\\wonye\\project\\AI\\KAWK50M\\artifacts\\benchmark-results\\20260809T050630Z\\kawk500m-instruct-v1\\results.json" | |
| } | |
| ], | |
| "completed_at_utc": "2026-08-09T05:21:38.806445+00:00" | |
| } |