Merge pull request #43 from drunkenbot-ai/develop

Develop

Author: drunkenbot-ai

.github/workflows/release.yml

@@ -45,6 +45,7 @@ jobs:
           python -m pip install --upgrade pip
           python -m pip install -r requirements.txt
           python -m pip install pyinstaller
+          python -m pip install Pillow
 
       - name: Build installer
         run: python packager.py

interface/tabs/dataset_tab.py

@@ -4,6 +4,7 @@
 
 from PySide6.QtCore import Qt
 from PySide6.QtWidgets import (
+    QApplication,
     QCheckBox,
     QComboBox,
     QFormLayout,
@@ -111,7 +112,8 @@ def build_dataset_tab(window) -> QWidget:
     window._tip(window.auto_vocab_label, "The actual vocabulary size selected after reading the corpus.")
     window.min_frequency = window._spin(1, 1000, 2)
     window._tip(window.min_frequency, "Minimum token frequency for tokenizer training. Higher values remove rare fragments and can reduce noise.")
-    window.context_length = window._spin(16, 1000, 128)
+    context_max = 1000 if not bool(QApplication.instance().property("license_valid")) else 1_000_000
+    window.context_length = window._spin(16, context_max, 128)
     window._tip(window.context_length, "Number of tokens per training sequence. Longer context lets the model learn longer dependencies but uses more memory.")
     window.validation_split = window._double_spin(0.0, 0.5, 0.1, 0.01, 3)
     window._tip(window.validation_split, "Fraction of tokens held out for validation. Validation helps detect overfitting during training.")

interface/tabs/training_tab.py

@@ -4,6 +4,7 @@
 
 from PySide6.QtCore import Qt, QTimer
 from PySide6.QtWidgets import (
+    QApplication,
     QCheckBox,
     QComboBox,
     QFormLayout,
@@ -113,7 +114,8 @@ def build_training_tab(window) -> QWidget:
     window._tip(window.attention_window, "Sliding attention window. 0 uses full context; higher values restrict attention to recent tokens.")
     window.n_layer = window._spin(1, 64, 4)
     window._tip(window.n_layer, "Transformer layer count. More layers improve capacity and reasoning patterns but slow training.")
-    window.train_context_length = window._spin(16, 1000, 128)
+    context_max = 1000 if not bool(QApplication.instance().property("license_valid")) else 1_000_000
+    window.train_context_length = window._spin(16, context_max, 128)
     window._tip(window.train_context_length, "Training context length in tokens. Must fit your GPU/CPU memory.")
     window.dropout = window._double_spin(0.0, 0.9, 0.1, 0.01, 3)
     window._tip(window.dropout, "Dropout regularization. Higher values reduce overfitting but can slow learning.")

packager.py

@@ -55,7 +55,11 @@ def _prepare_icon(work_dir: Path) -> Path | None:
         from PIL import Image
         Image.open(source).convert("RGBA").save(icon, format="ICO", sizes=[(256, 256), (128, 128), (64, 64), (32, 32), (16, 16)])
     except ImportError:
-        return None
+        bundled_icon = ROOT / "interface" / "drunkenbot_llm_ide.ico"
+        if bundled_icon.exists():
+            shutil.copy2(bundled_icon, icon)
+        else:
+            return None
     return icon