Architecture
- Decoder-only Transformer (GPT-style)
- 12 layers
- Hidden size: 768
- Attention heads: 12
- Context length: 512
- Parameters: ~100M
Training
- Dataset: News articles (CNN/DailyMail – articles only)
- Objective: Causal Language Modeling
- Hardware: Google Colab GPU
- Precision: FP16
- Training steps: 2000
- Optimizations: Gradient checkpointing, gradient accumulation
Training Loss Curve
The training loss decreased steadily from approximately 9.1 to 5.3 over 2000 training steps, indicating stable convergence during from-scratch training of the 100M-parameter language model.
Intended Use
- Research
- Educational purposes
- Text generation experiments
Limitations
- Not instruction-tuned
- Trained for limited steps
- Outputs may be verbose or repetitive
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