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
| model_config: configs/model_500m.yaml | |
| tokenizer_dir: artifacts/kawk500m-sft-dataset/tokenizer | |
| data: | |
| sequence_length: 2048 | |
| hf_dataset: | |
| repo_id: Infinity08/KAWK500M-Korean-SFT-v1 | |
| config_name: default | |
| revision: 0a8e914359a063b916cfd9a3ee068a38ddcc1f79 | |
| train_split: train | |
| validation_split: validation | |
| shuffle_buffer: 10000 | |
| validation_sequences: 300 | |
| training: | |
| output_dir: runs/kawk500m-ko-instruct-v1-a100 | |
| seed: 5150 | |
| precision: bf16 | |
| per_device_batch_size: 12 | |
| gradient_accumulation_steps: 5 | |
| epochs: 2.0 | |
| max_steps: null | |
| learning_rate: 2.0e-5 | |
| min_learning_rate: 2.0e-6 | |
| warmup_ratio: 0.03 | |
| weight_decay: 0.01 | |
| adam_beta1: 0.9 | |
| adam_beta2: 0.95 | |
| adam_eps: 1.0e-8 | |
| max_grad_norm: 1.0 | |
| gradient_checkpointing: false | |
| log_every: 10 | |
| eval_every: 100 | |
| eval_batches: 20 | |
| save_every: 250 | |
| save_final: true | |
| torch_compile: false | |
| compile_mode: default | |