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: | |
| model_type: llama | |
| architectures: | |
| - LlamaForCausalLM | |
| vocab_size: 32000 | |
| hidden_size: 1280 | |
| intermediate_size: 3584 | |
| num_hidden_layers: 26 | |
| num_attention_heads: 20 | |
| num_key_value_heads: 5 | |
| max_position_embeddings: 2048 | |
| rms_norm_eps: 1.0e-6 | |
| rope_theta: 10000.0 | |
| hidden_act: silu | |
| attention_bias: false | |
| mlp_bias: false | |
| tie_word_embeddings: true | |
| bos_token_id: 1 | |
| eos_token_id: 2 | |
| pad_token_id: 3 | |
| initializer_range: 0.02 | |
| use_cache: false | |
| expected_parameters: 505350400 | |