File size: 7,200 Bytes
fb2d2f6 b9189f6 1fe6c41 b9189f6 207c4da fb2d2f6 e03fb62 ffee4cc e27b807 f735f15 ceada43 f0ab9e9 e03fb62 f3332d1 d4099df ff0b212 d4099df |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 |
---
datasets:
- EleutherAI/the_pile_deduplicated
language:
- en
---
# broken bc of updates to transformers library, let me reimplement and train
GLORT2 (GLORT2 Low Rank Transformer Transformer) is a transformer model where every single linear layer is another smaller transformer model. I combined qkv into one operation which means one transformer instead of 3 to save on parameters, I played w using a transformer on the embeddings but it wasnt .. great, it's 768 dim 10 layers w/ 384 dim 1 layer as the replacements for linear layers (besides embed and lm head)
also sorry I just realized theres some residual from where I copied the model code from in my own projects that includes some "expanded lm head size" stuff just ignore that if you're looking at the config and code this isn't a serious project so I don't care too much that it's there
| model | 512-token strided perplexity on a pile test set | tokens |
| --- | --- | --- |
| cerebras 111m | 21.550655364990234 | 2.2b |
| cerebras 256m | 15.203496932983398 | 5.1b |
| cerebras 590m | 12.098200798034668 | 11.something b |
| deduped pythia 70m (95.6M) | 22.393400192260742 | 300b |
| deduped pythia 160m (213M) | 13.933751106262207 | 300b |
| deduped pythia 410m (506M) | 9.61842155456543 | 300b |
| llama w same settings as cerebras 111m (119m) | 13.882301330566406 | 2.2b |
| llama plus w same settings as cerebras 111m and llama 70b embeddings (369m) | 13.565109252929688 | 2.2b |
| **GLORT2 (205m)** | 13.051741600036621 | 2.2b |
| Tasks |Version|Filter|n-shot| Metric |Value | |Stderr|
|-------------|------:|------|-----:|--------|-----:|---|-----:|
|arc_challenge| 1|none | 25|acc |0.1706|± |0.0110|
| | |none | 25|acc_norm|0.2099|± |0.0119|
|truthfulqa_mc2| 2|none | 0|acc |0.4599|± |0.0154|
|winogrande| 1|none | 5|acc |0.5083|± |0.0141|
|hellaswag| 1|none | 10|acc |0.2728|± |0.0044|
| | |none | 10|acc_norm|0.2815|± |0.0045|
|gsm8k| 2|get-answer| 5|exact_match| 0|± | 0|
### mmlu
mean is 0.26394385964912276 i think
| Tasks |Version|Filter|n-shot|Metric|Value | |Stderr|
|-----------------------------------|------:|------|-----:|------|-----:|---|-----:|
|world_religions | 0|none | 5|acc |0.1988|± |0.0306|
|virology | 0|none | 5|acc |0.1928|± |0.0307|
|us_foreign_policy | 0|none | 5|acc |0.2600|± |0.0441|
|sociology | 0|none | 5|acc |0.2438|± |0.0304|
|security_studies | 0|none | 5|acc |0.4000|± |0.0314|
|public_relations | 0|none | 5|acc |0.2273|± |0.0401|
|professional_psychology | 0|none | 5|acc |0.2484|± |0.0175|
|professional_medicine | 0|none | 5|acc |0.4485|± |0.0302|
|professional_law | 0|none | 5|acc |0.2445|± |0.0110|
|professional_accounting | 0|none | 5|acc |0.2482|± |0.0258|
|prehistory | 0|none | 5|acc |0.2562|± |0.0243|
|philosophy | 0|none | 5|acc |0.2186|± |0.0235|
|nutrition | 0|none | 5|acc |0.2941|± |0.0261|
|moral_scenarios | 0|none | 5|acc |0.2503|± |0.0145|
|moral_disputes | 0|none | 5|acc |0.1965|± |0.0214|
|miscellaneous | 0|none | 5|acc |0.2554|± |0.0156|
|medical_genetics | 0|none | 5|acc |0.3000|± |0.0461|
|marketing | 0|none | 5|acc |0.1966|± |0.0260|
|management | 0|none | 5|acc |0.1942|± |0.0392|
|machine_learning | 0|none | 5|acc |0.2321|± |0.0401|
|logical_fallacies | 0|none | 5|acc |0.2331|± |0.0332|
|jurisprudence | 0|none | 5|acc |0.2407|± |0.0413|
|international_law | 0|none | 5|acc |0.3719|± |0.0441|
|human_sexuality | 0|none | 5|acc |0.2137|± |0.0360|
|human_aging | 0|none | 5|acc |0.2646|± |0.0296|
|high_school_world_history | 0|none | 5|acc |0.2489|± |0.0281|
|high_school_us_history | 0|none | 5|acc |0.2304|± |0.0296|
|high_school_statistics | 0|none | 5|acc |0.4722|± |0.0340|
|high_school_psychology | 0|none | 5|acc |0.3083|± |0.0198|
|high_school_physics | 0|none | 5|acc |0.3046|± |0.0376|
|high_school_microeconomics | 0|none | 5|acc |0.3361|± |0.0307|
|high_school_mathematics | 0|none | 5|acc |0.2630|± |0.0268|
|high_school_macroeconomics | 0|none | 5|acc |0.3231|± |0.0237|
|high_school_government_and_politics| 0|none | 5|acc |0.3523|± |0.0345|
|high_school_geography | 0|none | 5|acc |0.3384|± |0.0337|
|high_school_european_history | 0|none | 5|acc |0.2909|± |0.0355|
|high_school_computer_science | 0|none | 5|acc |0.2600|± |0.0441|
|high_school_chemistry | 0|none | 5|acc |0.2709|± |0.0313|
|high_school_biology | 0|none | 5|acc |0.3161|± |0.0265|
|global_facts | 0|none | 5|acc |0.1800|± |0.0386|
|formal_logic | 0|none | 5|acc |0.1667|± |0.0333|
|elementary_mathematics | 0|none | 5|acc |0.2540|± |0.0224|
|electrical_engineering | 0|none | 5|acc |0.3103|± |0.0386|
|econometrics | 0|none | 5|acc |0.2895|± |0.0427|
|conceptual_physics | 0|none | 5|acc |0.2553|± |0.0285|
|computer_security | 0|none | 5|acc |0.1900|± |0.0394|
|college_physics | 0|none | 5|acc |0.3431|± |0.0472|
|college_medicine | 0|none | 5|acc |0.2312|± |0.0321|
|college_mathematics | 0|none | 5|acc |0.1800|± |0.0386|
|college_computer_science | 0|none | 5|acc |0.3000|± |0.0461|
|college_chemistry | 0|none | 5|acc |0.2900|± |0.0456|
|college_biology | 0|none | 5|acc |0.2083|± |0.0340|
|clinical_knowledge | 0|none | 5|acc |0.2038|± |0.0248|
|business_ethics | 0|none | 5|acc |0.2100|± |0.0409|
|astronomy | 0|none | 5|acc |0.1908|± |0.0320|
|anatomy | 0|none | 5|acc |0.2963|± |0.0394|
|abstract_algebra | 0|none | 5|acc |0.2000|± |0.0402| |