diff --git a/ch06/02_bonus_additional-experiments/README.md b/ch06/02_bonus_additional-experiments/README.md index 61f6e07..7205642 100644 --- a/ch06/02_bonus_additional-experiments/README.md +++ b/ch06/02_bonus_additional-experiments/README.md @@ -1,7 +1,7 @@ # Additional Experiments The table below adds experiments to answer additional questions about various design choices. The first row uses the same settings as the main chapter and is used as a reference. -For example, +For example, - comparing rows 1 and 2 answers the question: "What is the performance difference when we train the last or first token?"; - comparing rows 1 and 3 answers the question: "What is the performance difference when we train only the last layer instead of the last block?"; @@ -9,23 +9,22 @@ For example,   -| | Model | Weights | Trainable token | Trainable layers | Context length | Training acc | Validation acc | Test acc | Training time | CPU/GPU | -| ---- | ------------------ | ---------- | --------------- | ---------------- | ----------------------- | ------------ | -------------- | -------- | ------------- | ------- | -| 1 | gpt2-small (124M) | pretrained | last | last_block | longest train ex. (120) | 96.63% | 99.33% | 95.00% | 0.28 min | A100 | -| 2 | gpt2-small (124M) | pretrained | first | last_block | longest train ex. (120) | 78.46% | 80.54% | 75.00% | 0.28 min | A100 | -| 3 | gpt2-small (124M) | pretrained | last | last_layer | longest train ex. (120) | 78.65% | 79.87% | 72.00% | 0.25 min | A100 | -| 4 | gpt2-small (124M) | pretrained | last | all | longest train ex. (120) | 99.62% | 96.64% | 96.67% | 0.69 min | A100 | -| 5 | gpt2-medium (355M) | pretrained | last | last_block | longest train ex. (120) | 87.50% | 91.28% | 84.67% | 0.75 min | A100 | -| 6 | gpt2-large (774M) | pretrained | last | last_block | longest train ex. (120) | 99.52% | 98.66% | 96.67% | 1.50 min | A100 | -| 7 | gpt2-xl (1558M) | pretrained | last | last_block | longest train ex. (120) | 99.81% | 99.33% | 98.33% | 2.83 min | A100 | -| 8 | gpt2-small (124M) | random | last | all | longest train ex. (120) | 100% | 96.64% | 93.67% | 0.69 min | A100 | -| 9 | gpt2-small (124M) | pretrained | last | LoRA | longest train ex. (120) | 100.00% | 97.32% | 96.67% | 0.75 min | A100 | -| 10 | gpt2-small (124M) | pretrained | last | last_block | context length (1024) | 83.08% | 87.92% | 78.33% | 2.46 min | A100 | -| 11 | gpt2-small (124M) | pretrained | last | last_block | variable: no padding (batch size 1) | 100.00% | 98.66% | 98.00% | 1.75 min | A100 | -| 12 | gpt2-small (124M) | pretrained | last | last_block | variable: no padding (batch size 8) | 99.33% | 98.66% | 98.33% | 1.70 min | A100 | -| 13 | gpt2-small (124M) | pretrained | last | last_block | longest train ex. (120); but no causal mask | 99.23% | 98.66% | 95.33% | 0.29 min | A100 | -| 14 | gpt2-small (124M) | pretrained | last | last_block | longest train ex. (120) and `ignore_index` for padding | 96.63% | 99.33% | 95.00% | 0.28 min | A100 | - +| | Model | Weights | Trainable token | Trainable layers | Context length | Training acc | Validation acc | Test acc | Training time | CPU/GPU | +| --- | ------------------ | ---------- | --------------- | ---------------- | ------------------------------------------------------ | ------------ | -------------- | -------- | ------------- | ------- | +| 1 | gpt2-small (124M) | pretrained | last | last_block | longest train ex. (120) | 96.63% | 99.33% | 95.00% | 0.28 min | A100 | +| 2 | gpt2-small (124M) | pretrained | first | last_block | longest train ex. (120) | 78.46% | 80.54% | 75.00% | 0.28 min | A100 | +| 3 | gpt2-small (124M) | pretrained | last | last_layer | longest train ex. (120) | 78.65% | 79.87% | 72.00% | 0.25 min | A100 | +| 4 | gpt2-small (124M) | pretrained | last | all | longest train ex. (120) | 99.62% | 96.64% | 96.67% | 0.69 min | A100 | +| 5 | gpt2-medium (355M) | pretrained | last | last_block | longest train ex. (120) | 87.50% | 91.28% | 84.67% | 0.75 min | A100 | +| 6 | gpt2-large (774M) | pretrained | last | last_block | longest train ex. (120) | 99.52% | 98.66% | 96.67% | 1.50 min | A100 | +| 7 | gpt2-xl (1558M) | pretrained | last | last_block | longest train ex. (120) | 99.81% | 99.33% | 98.33% | 2.83 min | A100 | +| 8 | gpt2-small (124M) | random | last | all | longest train ex. (120) | 100% | 96.64% | 93.67% | 0.69 min | A100 | +| 9 | gpt2-small (124M) | pretrained | last | LoRA | longest train ex. (120) | 100.00% | 97.32% | 96.67% | 0.75 min | A100 | +| 10 | gpt2-small (124M) | pretrained | last | last_block | context length (1024) | 83.08% | 87.92% | 78.33% | 2.46 min | A100 | +| 11 | gpt2-small (124M) | pretrained | last | last_block | variable: no padding (batch size 1) | 100.00% | 98.66% | 98.00% | 1.75 min | A100 | +| 12 | gpt2-small (124M) | pretrained | last | last_block | variable: no padding (batch size 8) | 99.33% | 98.66% | 98.33% | 1.70 min | A100 | +| 13 | gpt2-small (124M) | pretrained | last | last_block | longest train ex. (120); but no causal mask | 99.23% | 98.66% | 95.33% | 0.29 min | A100 | +| 14 | gpt2-small (124M) | pretrained | last | last_block | longest train ex. (120) and `ignore_index` for padding | 96.63% | 99.33% | 95.00% | 0.28 min | A100 |   @@ -34,7 +33,7 @@ For example, You can use the following code to reproduce the experiments: - Row 1: `python additional-experiments.py` -- Row 2: `python additional-experiments.py --trainable_token first` +- Row 2: `python additional-experiments.py --trainable_token first` - Row 3: `python additional-experiments.py --trainable_layers last_layer` - Row 4: `python additional-experiments.py --trainable_layers all` - Row 5: `python additional-experiments.py --model_size "gpt2-medium (355M)"`