Instructions to use heegyu/kodialogpt-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use heegyu/kodialogpt-v0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="heegyu/kodialogpt-v0")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("heegyu/kodialogpt-v0") model = AutoModelForCausalLM.from_pretrained("heegyu/kodialogpt-v0") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use heegyu/kodialogpt-v0 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "heegyu/kodialogpt-v0" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "heegyu/kodialogpt-v0", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/heegyu/kodialogpt-v0
- SGLang
How to use heegyu/kodialogpt-v0 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 "heegyu/kodialogpt-v0" \ --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": "heegyu/kodialogpt-v0", "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 "heegyu/kodialogpt-v0" \ --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": "heegyu/kodialogpt-v0", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use heegyu/kodialogpt-v0 with Docker Model Runner:
docker model run hf.co/heegyu/kodialogpt-v0
skt/kogpt2-base-v2λ₯Ό AIHub μΌμλν λ°μ΄ν°μ
μΌλ‘ νμΈνλν λͺ¨λΈμ
λλ€.
νμ΅ μ½λ: https://github.com/HeegyuKim/open-domain-dialog
Streamlit Demo: https://heegyukim-open-domain-dialog-st-demo-1tzktp.streamlitapp.com/
μ¬μ©μμ
tokenizer = AutoTokenizer.from_pretrained("heegyu/kodialogpt-v0")
model = AutoModelForCausalLM.from_pretrained("heegyu/kodialogpt-v0")
generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
generation_args = dict(
num_beams=4,
repetition_penalty=2.0,
no_repeat_ngram_size=4,
eos_token_id=375, # \n
max_new_tokens=64,
do_sample=True,
top_k=50,
early_stopping=True
)
generator(
["0 : **λ κ²μ μ’μνλ\n1 :",
"0 : μ΄μ κ°λ¨μμ μ΄μΈμ¬κ±΄ λ¬λ γ
γ
λ무 무μμ\n1 : ν μ? λ¬΄μ¨ μΌ μμμ΄?\n0 : μ¬μ§λ³΄λκΉ λ§ νΌν리λ μ¬λμκ³ κ²½μ°°λ€μ΄ λ μ μ μνκ³ λ리λ μλμλ€λλ°??\n1 :",
"0 : μκΈ°μΌ μ΄μ λ λνν
μ κ·Έλ¬μ΄?\n1 : λ μΌ μμμ΄?\n0 : μ΄λ»κ² λνν
λ§λ μμ΄ κ·Έλ΄ μ μμ΄? λ μ§μ§ μ€λ§νμ΄\n1 : "],
**generation_args
)
κ²°κ³Ό
[[{'generated_text': '0 : **λ κ²μ μ’μνλ\n1 : λλ κ²μμ μ μ ν΄ ν€ν€ '}],
[{'generated_text': '0 : μ΄μ κ°λ¨μμ μ΄μΈμ¬κ±΄ λ¬λ γ
γ
λ무 무μμ\n1 : ν μ? λ¬΄μ¨ μΌ μμμ΄?\n0 : μ¬μ§λ³΄λκΉ λ§ νΌν리λ μ¬λμκ³ κ²½μ°°λ€μ΄ λ μ μ μνκ³ λ리λ μλμλ€λλ°??\n1 : μμ΄κ³ ... μ§μ§ 무μλ€... '}],
[{'generated_text': '0 : μκΈ°μΌ μ΄μ λ λνν
μ κ·Έλ¬μ΄?\n1 : λ μΌ μμμ΄?\n0 : μ΄λ»κ² λνν
λ§λ μμ΄ κ·Έλ΄ μ μμ΄? λ μ§μ§ μ€λ§νμ΄\n1 : λ μλͺ» νκΈΈλ κ·Έλ? '}]]
νμ΅μ μ¬μ©ν νμ΄νΌνλΌλ―Έν°
- Downloads last month
- 2