Q-bert commited on
Commit
aa43490
·
verified ·
1 Parent(s): d3f2a71

Update app.py

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Files changed (1) hide show
  1. app.py +4 -4
app.py CHANGED
@@ -51,7 +51,7 @@ def check_existing_model(stock_symbol, start_date, end_date):
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  @spaces.GPU(duration=300)
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  def train_stock_model(stock_symbol, start_date, end_date, feature_range=(10, 100), data_seq_length=256, epochs=10, batch_size=16, learning_rate=2e-4):
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- repo_id = f"Q-bert/StockLlama-tuned-{stock_symbol}-{stock_symbol}-{start_date}_{end_date}"
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  if check_existing_model(stock_symbol, start_date, end_date):
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  return f"Model for {stock_symbol} from {start_date} to {end_date} already exists."
@@ -107,20 +107,20 @@ def train_stock_model(stock_symbol, start_date, end_date, feature_range=(10, 100
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  weight_decay=0.01,
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  lr_scheduler_type="linear",
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  seed=3407,
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- output_dir=f"StockLlama-LoRA-{stock_symbol}-{stock_symbol}-{start_date}_{end_date}",
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  ),
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  )
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  trainer.train()
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  model = model.merge_and_unload()
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- model.push_to_hub(f"Q-bert/StockLlama-tuned-{stock_symbol}-{stock_symbol}-{start_date}_{end_date}")
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  scaler_path = "scaler.joblib"
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  joblib.dump(scaler, scaler_path)
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  upload_file(
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  path_or_fileobj=scaler_path,
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  path_in_repo=f"scalers/{scaler_path}",
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- repo_id=f"Q-bert/StockLlama-tuned-{stock_symbol}-{stock_symbol}-{start_date}_{end_date}"
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  )
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  return f"Training completed and model saved for {stock_symbol} from {start_date} to {end_date}."
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  @spaces.GPU(duration=300)
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  def train_stock_model(stock_symbol, start_date, end_date, feature_range=(10, 100), data_seq_length=256, epochs=10, batch_size=16, learning_rate=2e-4):
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+ repo_id = f"Q-bert/StockLlama-tuned{stock_symbol}-{start_date}_{end_date}"
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  if check_existing_model(stock_symbol, start_date, end_date):
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  return f"Model for {stock_symbol} from {start_date} to {end_date} already exists."
 
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  weight_decay=0.01,
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  lr_scheduler_type="linear",
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  seed=3407,
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+ output_dir=f"StockLlama-LoRA-{stock_symbol}-{start_date}_{end_date}",
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  ),
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  )
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  trainer.train()
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  model = model.merge_and_unload()
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+ model.push_to_hub(f"Q-bert/StockLlama-tuned-{stock_symbol}-{start_date}_{end_date}")
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  scaler_path = "scaler.joblib"
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  joblib.dump(scaler, scaler_path)
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  upload_file(
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  path_or_fileobj=scaler_path,
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  path_in_repo=f"scalers/{scaler_path}",
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+ repo_id=f"Q-bert/StockLlama-tuned-{stock_symbol}-{start_date}_{end_date}"
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  )
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  return f"Training completed and model saved for {stock_symbol} from {start_date} to {end_date}."
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