Training in progress, step 100

LICENSE

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README.md

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+# StockLlama
+![image](https://github.com/user-attachments/assets/11d12a8f-63b8-42ce-b66c-d77924831e90)
+
+
+StockLlama is a time series forecasting model based on Llama, enhanced with custom embeddings for improved accuracy.
+
+# Usage:
+To use the **StockLlama**, follow these steps:
+
+1. Clone the repository to your local machine.
+   
+```bash
+git clone https://github.com/LegallyCoder/StockLlama
+```
+2. Open a terminal or command prompt and navigate to the script's directory.
+```bash
+cd src
+```
+
+3. Install the required packages using this command:
+
+```bash
+pip3 install -r requirements.txt
+```
+
+4. Open new python file at the script's directory.
+```python
+import yfinance as yf
+import torch
+import matplotlib.pyplot as plt
+import numpy as np
+from scipy.ndimage import gaussian_filter1d
+from datetime import datetime, timedelta
+from modeling_stockllama import StockLlamaForForecasting
+import pandas as pd
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+model = StockLlamaForForecasting.from_pretrained("StockLlama/StockLlama").to(device)
+day = 365
+def download_stock_data(stock_symbol):
+    end_date = datetime.today().date()
+    start_date = datetime.today().date() - timedelta(days=day)
+    try:
+        return yf.download(stock_symbol, start=start_date, end=end_date, progress=False)
+    except Exception as e:
+        print(f"Error downloading data for {stock_symbol}: {e}")
+        return None
+
+def predict_future_prices(stock_symbol):
+    stock_data = download_stock_data(stock_symbol)
+    if stock_data is not None:
+        subset = stock_data[['Close']].tail(day).reset_index(drop=True)
+        model.eval()
+
+        def prepare_data(data):
+            return torch.tensor(data.values, dtype=torch.float32).unsqueeze(0).to(device)
+
+        data_tensor = prepare_data(subset)
+        future_predictions = []
+
+        with torch.no_grad():
+            for _ in range(day):
+                output = model(data_tensor.squeeze(-1)).logits
+
+                if len(output.shape) == 3:
+                    last_prediction = output[:, -1, :].squeeze(0)
+                elif len(output.shape) == 2:
+                    last_prediction = output.squeeze(0)
+                else:
+                    raise ValueError("Unexpected model output shape.")
+
+                future_predictions.append(last_prediction.item())
+
+                if len(output.shape) == 3:
+                    data_tensor = torch.cat((data_tensor[:, 1:, :], output[:, -1, :].unsqueeze(1)), dim=1)
+                elif len(output.shape) == 2:
+                    data_tensor = torch.cat((data_tensor[:, 1:], last_prediction.unsqueeze(0).unsqueeze(0)), dim=1)
+        future_predictions = gaussian_filter1d(future_predictions, sigma=1)
+        combined_prices = pd.concat([subset['Close'], pd.Series(future_predictions)], ignore_index=True)
+        historical_dates = stock_data.index[-day:].to_list()
+        prediction_dates = [historical_dates[-1] + timedelta(days=i) for i in range(1, len(future_predictions) + 1)]
+        combined_dates = historical_dates + prediction_dates
+
+        plt.figure(figsize=(12, 6))
+        plt.plot(combined_dates[:len(subset)], combined_prices[:len(subset)], label='Historical Prices', linestyle='-')
+        plt.plot(combined_dates[len(subset)-1:], combined_prices[len(subset)-1:], label='Predicted Prices', linestyle='--')
+        plt.xlabel('Date')
+        plt.ylabel('Price')
+        plt.title(f'{stock_symbol} - Combined Historical and Predicted Prices')
+        plt.legend()
+        plt.grid(True)
+        plt.xticks(rotation=45)
+        plt.tight_layout()
+        plt.show()
+
+        return future_predictions
+    else:
+        print(f"Data could not be downloaded for {stock_symbol}.")
+        return None
+
+stock_symbol = 'AAPL'
+future_predictions = predict_future_prices(stock_symbol)
+
+```
+## Result
+
+![image](https://github.com/user-attachments/assets/92437257-5473-4717-8411-4b7d1baf9978)
+**WARNING:** This model is just a prediction model. I cannot accept any responsibility.
+
+# Training Code:
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1a8i6bOKRw9h-gzO4S1GkRa71mZITuMge?usp=sharing)
+
+# Fine-tuning Space:
+Using ZeroGPU support and LoRA training with any stock market. (You can find stock symbols on Yahoo Finance)
+
+[Hugging Face Space](https://huggingface.co/spaces/Q-bert/StockLlama-TrainOnAnyStock)
+
+For LoRA trained models, You can look [StockLlama](https://huggingface.co/StockLlama) organization.
+
+# For more:
+
+You can reach me on,
+
+[Linkedin](https://www.linkedin.com/in/talha-r%C3%BCzgar-akku%C5%9F-1b5457264/)
+
+[Twitter](https://x.com/TalhaRuzga35606)
+
+[Hugging Face](https://huggingface.co/Q-bert)

config.json

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+{
+  "architectures": [
+    "StockLlamaForForecasting"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 5000,
+  "max_position_embeddings": 2048,
+  "mlp_bias": false,
+  "model_type": "stockllama",
+  "num_attention_heads": 8,
+  "num_hidden_layers": 8,
+  "num_key_value_heads": 8,
+  "pad_token_id": 0,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "term_number": 4,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.44.2",
+  "use_cache": true,
+  "vocab_size": 10000
+}

model.safetensors

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+size 707739976

src/requirements.txt

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+yfinance
+pandas
+numpy
+torch
+joblib
+tqdm
+datasets
+transformers
+huggingface-hub
+wandb
+peft

training_args.bin

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