| | --- |
| | language: |
| | - ko |
| | - en |
| | base_model: |
| | - LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct |
| | --- |
| | ### How to Load |
| | ```python |
| | import torch |
| | from transformers import AutoModelForCausalLM, AutoTokenizer |
| | model = AutoModelForCausalLM.from_pretrained( |
| | "hmlee/exaone_prune_sft", |
| | torch_dtype=torch.bfloat16, |
| | trust_remote_code=True, |
| | device_map="auto" |
| | ) |
| | tokenizer = AutoTokenizer.from_pretrained("LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct") |
| | ``` |
| | ### How to use |
| | ```python |
| | |
| | # Choose your prompt |
| | prompt = "Explain who you are" # English example |
| | prompt = "λμ μμμ λ§ν΄λ΄" # Korean example |
| | |
| | messages = [ |
| | {"role": "system", |
| | "content": "You are EXAONE model from LG AI Research, a helpful assistant."}, |
| | {"role": "user", "content": prompt} |
| | ] |
| | input_ids = tokenizer.apply_chat_template( |
| | messages, |
| | tokenize=True, |
| | add_generation_prompt=True, |
| | return_tensors="pt" |
| | ) |
| | |
| | output = model.generate( |
| | input_ids.to(model.device), |
| | eos_token_id=tokenizer.eos_token_id, |
| | max_new_tokens=128 |
| | ) |
| | print(tokenizer.decode(output[0])) |
| | ``` |