# Uploaded  model

- **Developed by:** formapproval
- **License:** apache-2.0
- **Finetuned from model :** llm-jp/llm-jp-3-13b



使い方  
前提：Omnicampus上で行う・elyza-tasks-100-TV_0.jsonlをルートディレクトリ上に配置  
手順 以下のコードをipynbファイルで、ルートディレクトリ上で実行  
!pip install -U pip  
!pip install -U transformers  
!pip install -U bitsandbytes  
!pip install -U accelerate  
!pip install -U datasets  
!pip install -U peft  
!pip install -U trl  
!pip install -U wandb  
!pip install ipywidgets --upgrade  
from transformers import AutoModelForCausalLM  
import os, torch, gc  
from datasets import load_dataset  
import bitsandbytes as bnb  
from trl import SFTTrainer  
base_model_id = "llm-jp/llm-jp-3-13b"  
HF_TOKEN="~~~"#オープンサイトでは伝えられなかったので、後で伝える形になります  
model = AutoModelForCausalLM.from_pretrained(  
base_model_id,  
token=HF_TOKEN,  
quantization_config=bnb_config,  
device_map="auto"  
)  
import json  
datasets = []  
with open("./elyza-tasks-100-TV_0.jsonl", "r") as f:  
item = ""  
for line in f:  
line = line.strip()  
item += line  
if item.endswith("}"):  
datasets.append(json.loads(item))  
item = ""  
from tqdm import tqdm  

results = []  
for data in tqdm(datasets):  

input = data["input"]  

prompt = f"""### 指示  
{input}  
"""  
  
tokenized_input = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt").to(model.device)  
attention_mask = torch.ones_like(tokenized_input)  
  
with torch.no_grad():  
outputs = model.generate(  
tokenized_input,  
attention_mask=attention_mask,  
max_new_tokens=100,  
do_sample=False,  
repetition_penalty=1.2,  
pad_token_id=tokenizer.eos_token_id  
)[0]  
output = tokenizer.decode(outputs[tokenized_input.size(1):], skip_special_tokens=True)  

results.append({"task_id": data["task_id"], "input": input, "output": output})  
import re  
jsonl_id = re.sub(".*/", "", new_model_id)  
with open(f"./{jsonl_id}-outputs.jsonl", 'w', encoding='utf-8') as f:  
for result in results:  
json.dump(result, f, ensure_ascii=False)  
f.write('\n')