CrystalLM

This release marks the final iteration of CrystalLM using a single-layer GRU architecture.
The focus of this version is stability, memory efficiency, and clarity of the training pipeline, before moving on to deeper or more advanced models.

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✨ Highlights

• Single-layer GRU architecture

  • Simple, interpretable, and stable
  • Serves as a clean baseline for future multi-layer or Transformer-based experiments

• Significantly reduced memory usage

  • Training memory footprint reduced from ~16 GB to ~350 MB
  • Enables training on consumer-grade hardware

• Expanded training corpus

  • ~125k tokens
  • ~200k characters
  • Vocabulary size: ~14k
  • Improved output diversity compared to earlier versions

• Stable convergence

  • Loss consistently drops from ~6.6 to ~2.6 within a few epochs
  • No observed exploding or vanishing gradients

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📊 Training Snapshot

Prefix dict has been built successfully.
词数： 125425
训练文本总长度（字符数）：200945
训练文本总行数：805
训练文本独立字符数(vocab size)：3119
词表大小: 14202
epoch 0, loss 6.6937, [23:18:20]
epoch 1, loss 5.4224, [23:23:00]
epoch 2, loss 4.3564, [23:27:31]
epoch 3, loss 3.3981, [23:30:59]
epoch 4, loss 2.6177, [23:33:42]
✅ 模型已保存
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