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# ==============================================================================
# COPYRIGHT (C) 2025 KONSTANTIN VLADIMIROVICH GRABKO. ALL RIGHTS RESERVED.
# PATENT PENDING | CMS MANHATTAN JIRACK TECHNOLOGY
#
# This software is licensed under the Commercial License Agreement V.1.2.
# Any use, modification, or distribution of this code requires compliance with
# the terms found in the LICENSE.md file in the root directory.
#
# NO PATENTING RIGHTS: Users are strictly prohibited from filing patent claims
# based on the BRE or SWA architectures disclosed herein.
# Contact: grabko@cmsmanhattan.com | +1 (516) 777-0945
# ==============================================================================
import matplotlib.pyplot as plt
import re
import os
LOG_FILE = "fine_tune_pile.log"
OUTPUT_IMAGE = "loss_chart.png"
def plot_loss():
if not os.path.exists(LOG_FILE):
print(f"Файл {LOG_FILE} не найден.")
return
steps = []
losses = []
# Регулярное выражение для поиска шага и лосса
pattern = re.compile(r"Шаг (\d+).*Loss: (\d+\.\d+)")
with open(LOG_FILE, "r") as f:
for line in f:
match = pattern.search(line)
if match:
steps.append(int(match.group(1)))
losses.append(float(match.group(2)))
if not steps:
print("Данные для графика пока не найдены в логе.")
return
plt.figure(figsize=(10, 6))
plt.plot(steps, losses, label='Raw Loss', alpha=0.3, color='blue')
# Скользящее среднее для сглаживания
if len(losses) > 10:
window = 10
smooth_loss = [sum(losses[i:i+window])/window for i in range(len(losses)-window)]
plt.plot(steps[window:], smooth_loss, label='Smooth Loss (EMA)', color='red', linewidth=2)
plt.xlabel('Step')
plt.ylabel('Loss')
plt.title('JiRack 1B Training Progress')
plt.legend()
plt.grid(True, linestyle='--', alpha=0.6)
plt.savefig(OUTPUT_IMAGE)
print(f"График сохранен в {OUTPUT_IMAGE}")
if __name__ == "__main__":
plot_loss() |