Dataset Viewer
Model
stringclasses 9
values | #Model Parameters (B)
int64 1
70
| Draft (Assistant)
stringclasses 10
values | #Draft Parameters (B)
float64 0.27
13
⌀ | Task
stringclasses 1
value | Total Parameter Size (B)
float64 1
83
| Speculative
Average time per input (ms)
float64 1.2k
6.01k
| Speculative
Average time per token (ms)
float64 9.96
55
| Original
Average time per input (ms)
float64 2.15k
12.4k
| Original
Average time per token (ms)
float64 17.9
114
| Speedup
float64 1
2.84
| Command
stringlengths 93
109
|
|---|---|---|---|---|---|---|---|---|---|---|---|
meta-llama/Llama-2-7b-hf
| 7
|
TinyLlama/TinyLlama_v1.1
| 1
|
summarization
| 8
| 2,771.54
| 21.65
| 3,368.48
| 26.32
| 1.22
|
python benchmark_decoder_summ.py meta-llama/Llama-2-7b-hf --aux-model TinyLlama/TinyLlama_v1.1 --dtype fp16
|
meta-llama/Llama-2-7b-hf
| 7
|
apple/OpenELM-270M
| 0.27
|
summarization
| 7.27
| 2,607.82
| 20.37
| 4,221.14
| 32.98
| 1.62
|
python benchmark_decoder_summ.py meta-llama/Llama-2-7b-hf --aux-model apple/OpenELM-270M --dtype fp16
|
meta-llama/Llama-2-7b-hf
| 7
|
apple/OpenELM-450M
| 0.45
|
summarization
| 7.45
| 3,324.68
| 25.97
| 4,178.66
| 32.65
| 1.26
|
python benchmark_decoder_summ.py meta-llama/Llama-2-7b-hf --aux-model apple/OpenELM-450M --dtype fp16
|
facebook/layerskip-llama2-7B
| 7
|
Early Exit @ Layer 4
| null |
summarization
| 7
| 2,548.4
| 19.91
| 3,306.73
| 25.83
| 1.297338
|
python benchmark_decoder_summ.py facebook/layerskip-llama2-7B --aux-early-exit 4 --dtype fp16
|
meta-llama/Llama-2-13b-hf
| 13
|
meta-llama/Llama-2-7b-hf
| 7
|
summarization
| 20
| 3,557.07
| 27.79
| 4,088.48
| 31.94
| 1.149334
|
python benchmark_decoder_summ.py meta-llama/Llama-2-13b-hf --aux-model meta-llama/Llama-2-7b-hf --dtype fp16
|
meta-llama/Llama-2-13b-hf
| 13
|
TinyLlama/TinyLlama_v1.1
| 1
|
summarization
| 14
| 2,901.92
| 22.67
| 4,190.42
| 32.74
| 1.444199
|
python benchmark_decoder_summ.py meta-llama/Llama-2-13b-hf --aux-model TinyLlama/TinyLlama_v1.1 --dtype fp16
|
meta-llama/Llama-2-13b-hf
| 13
|
apple/OpenELM-270M
| 0.27
|
summarization
| 13.27
| 2,883.33
| 22.53
| 4,521.12
| 35.32
| 1.567688
|
python benchmark_decoder_summ.py meta-llama/Llama-2-13b-hf --aux-model apple/OpenELM-270M --dtype fp16
|
meta-llama/Llama-2-13b-hf
| 13
|
apple/OpenELM-450M
| 0.45
|
summarization
| 13.45
| 3,267.69
| 25.53
| 4,321.75
| 33.76
| 1.322366
|
python benchmark_decoder_summ.py meta-llama/Llama-2-13b-hf --aux-model apple/OpenELM-450M --dtype fp16
|
facebook/layerskip-llama2-13B
| 13
|
Early Exit @ Layer 4
| null |
summarization
| 13
| 4,238.45
| 33.11
| 4,217.78
| 32.95
| 0.995168
|
python benchmark_decoder_summ.py facebook/layerskip-llama2-13B --aux-early-exit 4 --dtype fp16
|
facebook/layerskip-llama2-13B
| 13
|
Early Exit @ Layer 8
| null |
summarization
| 13
| 2,459.61
| 19.22
| 4,294.98
| 33.55
| 1.745578
|
python benchmark_decoder_summ.py facebook/layerskip-llama2-13B --aux-early-exit 8 --dtype fp16
|
facebook/layerskip-llama3.2-1B
| 1
|
Early Exit @ Layer 4
| null |
summarization
| 1
| 1,195.28
| 9.96
| 2,147.7
| 17.9
| 1.8
|
python benchmark_decoder_summ.py facebook/layerskip-llama3.2-1B --aux-early-exit 4 --dtype fp16
|
meta-llama/Meta-Llama-3-8B
| 8
|
meta-llama/Llama-3.2-1B
| 1
|
summarization
| 9
| 1,872.46
| 19.04
| 2,859.35
| 29.08
| 1.53
|
python benchmark_decoder_summ.py meta-llama/Meta-Llama-3-8B --aux-model meta-llama/Llama-3.2-1B --dtype fp16
|
meta-llama/Meta-Llama-3-8B
| 8
|
meta-llama/Llama-3.2-3B
| 3
|
summarization
| 11
| 2,814.82
| 28.63
| 2,825.36
| 28.73
| 1
|
python benchmark_decoder_summ.py meta-llama/Meta-Llama-3-8B --aux-model meta-llama/Llama-3.2-3B --dtype fp16
|
facebook/layerskip-llama3-8B
| 8
|
Early Exit @ Layer 4
| null |
summarization
| 8
| 1,949.02
| 15.75
| 3,571.81
| 28.87
| 1.83
|
python benchmark_decoder_summ.py facebook/layerskip-llama3-8B --aux-early-exit 4 --dtype fp16
|
meta-llama/Llama-2-70b-hf
| 70
|
meta-llama/Llama-2-13b-hf
| 13
|
summarization
| 83
| 5,036.54
| 46.3
| 12,289.01
| 112.97
| 2.439957
|
python benchmark_decoder_summ.py meta-llama/Llama-2-70b-hf --aux-model meta-llama/Llama-2-13b-hf --dtype fp16
|
meta-llama/Llama-2-70b-hf
| 70
|
meta-llama/Llama-2-7b-hf
| 7
|
summarization
| 77
| 4,357.55
| 40.06
| 12,324.19
| 113.3
| 2.828258
|
python benchmark_decoder_summ.py meta-llama/Llama-2-70b-hf --aux-model meta-llama/Llama-2-7b-hf --dtype fp16
|
meta-llama/Llama-2-70b-hf
| 70
|
TinyLlama/TinyLlama_v1.1
| 1
|
summarization
| 71
| 4,356.21
| 40.05
| 12,363.22
| 113.66
| 2.837953
|
python benchmark_decoder_summ.py meta-llama/Llama-2-70b-hf --aux-model TinyLlama/TinyLlama_v1.1 --dtype fp16
|
facebook/layerskip-llama2-70B
| 70
|
Early Exit @ Layer 10
| null |
summarization
| 70
| 6,012.04
| 54.96
| 12,383.34
| 113.2
| 2.06
|
python benchmark_decoder_summ.py facebook/layerskip-llama2-70B --aux-early-exit 10 --dtype fp16
|
LayerSkip Resources
This dataset holds some of the assets that support the blog post on LayerSkip.
Contents:
- early_exit_self_speculative_decoding.ipynb: Notebook that deeps dive into the working of LayerSkip
- summarization.csv: A CSV containing the benchmark results for (self) speculative-decoding strategies.
These resources were created by Mostafa (the first author of LayerSkip). Thanks a lot! 🤗
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