|
|
--- |
|
|
language: |
|
|
- ko |
|
|
- en |
|
|
base_model: |
|
|
- LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct |
|
|
--- |
|
|
### How to Load |
|
|
```python |
|
|
import torch |
|
|
from transformers import AutoModelForCausalLM |
|
|
model = AutoModelForCausalLM.from_pretrained( |
|
|
"hmlee/exaone_pruned", |
|
|
torch_dtype=torch.bfloat16, |
|
|
trust_remote_code=True, |
|
|
device_map="auto" |
|
|
) |
|
|
``` |
|
|
### How to use |
|
|
```python |
|
|
import torch |
|
|
from transformers import AutoTokenizer |
|
|
|
|
|
tokenizer = AutoTokenizer.from_pretrained("LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct") |
|
|
|
|
|
# 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])) |
|
|
``` |