GPTfree api commited on
Commit
ea1604a
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1 Parent(s): d3c49d5

Update app.py

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Files changed (1) hide show
  1. app.py +8 -7
app.py CHANGED
@@ -2,30 +2,30 @@ import os
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  import tempfile
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  from vllm import LLM
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  from vllm.sampling_params import SamplingParams
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- from huggingface_hub import hf_hub_download
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  from datetime import datetime, timedelta
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  # モデル名
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  model_name = "mistralai/Mistral-7B-Instruct-v0.2"
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- # SYSTEM_PROMPTのロード関数
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- def load_system_prompt(repo_id: str, filename: str) -> str:
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- file_path = hf_hub_download(repo_id=repo_id, filename=filename)
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  with open(file_path, 'r') as file:
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  system_prompt = file.read()
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  today = datetime.today().strftime('%Y-%m-%d')
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  yesterday = (datetime.today() - timedelta(days=1)).strftime('%Y-%m-%d')
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- model_name = repo_id.split("/")[-1]
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  return system_prompt.format(name=model_name, today=today, yesterday=yesterday)
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  # 一時ディレクトリをキャッシュ用に設定
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  with tempfile.TemporaryDirectory() as tmpdirname:
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  os.environ["TRANSFORMERS_CACHE"] = tmpdirname
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  os.environ["HF_HOME"] = tmpdirname
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  os.environ["HF_TOKEN"] = os.getenv("HF_TOKEN") # 環境変数からトークンを取得
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- SYSTEM_PROMPT = load_system_prompt(model_name, "SYSTEM_PROMPT.txt")
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-
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  messages = [
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  {"role": "system", "content": SYSTEM_PROMPT},
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  {
@@ -40,4 +40,5 @@ with tempfile.TemporaryDirectory() as tmpdirname:
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  llm = LLM(model=model_name, trust_remote_code=True, tensor_parallel_size=1, device="cpu")
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  outputs = llm.chat(messages, sampling_params=sampling_params)
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  print(outputs[0].outputs[0].text)
 
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  import tempfile
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  from vllm import LLM
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  from vllm.sampling_params import SamplingParams
 
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  from datetime import datetime, timedelta
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  # モデル名
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  model_name = "mistralai/Mistral-7B-Instruct-v0.2"
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+ # SYSTEM_PROMPTのロード関数 (ローカルファイルを読み込む)
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+ def load_system_prompt(file_path: str) -> str:
 
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  with open(file_path, 'r') as file:
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  system_prompt = file.read()
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  today = datetime.today().strftime('%Y-%m-%d')
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  yesterday = (datetime.today() - timedelta(days=1)).strftime('%Y-%m-%d')
 
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  return system_prompt.format(name=model_name, today=today, yesterday=yesterday)
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+ # SYSTEM_PROMPT.txtは現在のディレクトリ内にあることを想定
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+ system_prompt_path = "./SYSTEM_PROMPT.txt" # 現在のディレクトリ内のファイルパス
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+ SYSTEM_PROMPT = load_system_prompt(system_prompt_path)
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+
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  # 一時ディレクトリをキャッシュ用に設定
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  with tempfile.TemporaryDirectory() as tmpdirname:
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  os.environ["TRANSFORMERS_CACHE"] = tmpdirname
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  os.environ["HF_HOME"] = tmpdirname
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  os.environ["HF_TOKEN"] = os.getenv("HF_TOKEN") # 環境変数からトークンを取得
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+ # メッセージリストの作成
 
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  messages = [
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  {"role": "system", "content": SYSTEM_PROMPT},
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  {
 
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  llm = LLM(model=model_name, trust_remote_code=True, tensor_parallel_size=1, device="cpu")
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  outputs = llm.chat(messages, sampling_params=sampling_params)
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+ # 出力を表示
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  print(outputs[0].outputs[0].text)