
# open-llama-2-ko based model with inhouse dataset 

This is an Korean Model based on
* [beomi/open-llama-2-ko-7b]

gpu code example

```
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
import math

## v2 models
model_path = "colable/llama-ko-peft-v0.2"

tokenizer = AutoTokenizer.from_pretrained(model_path, use_default_system_prompt=False)
model = AutoModelForCausalLM.from_pretrained(
    model_path, torch_dtype=torch.float32, device_map='auto',local_files_only=False, load_in_4bit=True
)
print(model)
prompt = input("please input prompt:")
while len(prompt) > 0:
  input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to("cuda")

  generation_output = model.generate(
    input_ids=input_ids, max_new_tokens=500,repetition_penalty=1.2
  )
  print(tokenizer.decode(generation_output[0]))
  prompt = input("please input prompt:")
```
{"language": ["ko"], "license": "mit"}
transformers safetensors llama text-generation ko license:mit autotrain_compatible endpoints_compatible text-generation-inference region:us