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Zhangir Azerbayev, Hailey Schoelkopf, Keiran Paster, Marco Dos Santos, Stephen McAleer, Albert Q. Jiang, Jia Deng, Stella Biderman, Sean Welleck
Llemma 34B is a language model for mathematics. It was initialized with Code Llama 34B weights, and trained on the Proof-Pile-2 for 50B tokens.
This model also comes in a 7B parameter version: Llemma 7B.
Evaluations
Llemma models are particularly strong at chain-of-thought mathematical reasoning and using computational tools for mathematics, such as Python and formal theorem provers.
Chain-of-thought Math
On chain-of-thought mathematics tasks, Llemma models outperform Llama-2, Code Llama, and when controlled for model size, outperform Minerva.
| Model | Size | GSM8k | OCW | MMLU-STEM | SAT | MATH |
|---|---|---|---|---|---|---|
| Llama 2 | 7B | 11.8% | 3.7% | 29.9% | 25% | 3.2% |
| Code Llama | 7B | 10.5% | 4.4% | 25.1% | 9.4% | 4.5% |
| LLEMMA | 7B | 36.4% | 7.7% | 37.7% | 53.1% | 18.0% |
| Minerva | 8B | 16.2% | 7.7% | 35.6% | - | 14.1% |
| ------------ | ------ | -------- | ------- | ----------- | ------- | ------- |
| Code Llama | 34B | 29.6% | 7.0% | 40.5% | 40.6% | 12.2% |
| LLEMMA | 34B | 51.5% | 11.8% | 49.0% | 71.9% | 25.0% |
| ------------ | ------ | -------- | ------- | ----------- | ------- | ------- |
| Minerva | 62B | 52.4% | 12.0% | 53.9% | - | 27.6% |
| Minerva | 540B | 58.8% | 17.6% | 63.9% | - | 33.6% |
Further performance can be extracted by using majority voting:
| Model | Size | GSM8k maj@100 | OCW maj@100 | MMLU-STEM maj@16 | SAT maj@16 | MATH maj@256 |
|---|---|---|---|---|---|---|
| LLEMMA | 7B | 54.0% | 14.3% | 49.9% | 78.1% | 33.5 |
| Minerva | 8B | 28.4% | 12.5% | 43.4% | - | 25.4% |
| --------- | ------ | ------------- | ----------- | ----------------- | ----------- | ------------ |
| LLEMMA | 34B | 69.3% | 18.4% | 59.7% | 81.3% | 43.1% |
| --------- | ------ | ------------- | ----------- | ----------------- | ----------- | ------------ |
| Minerva | 62B | 68.5% | 23.5% | 63.5% | - | 43.4% |
| Minerva | 540B | 78.5% | 30.8% | 75.0% | - | 50.3% |
Tool Use and Theorem Proving
In addition to chain-of-thought reasoning, Llemma has strong capabilities in computational mathematics tasks. For tool use and formal theorem proving evaluations, see our paper.
Citation
@misc{azerbayev2023llemma,
title={Llemma: An Open Language Model For Mathematics},
author={Zhangir Azerbayev and Hailey Schoelkopf and Keiran Paster and Marco Dos Santos and Stephen McAleer and Albert Q. Jiang and Jia Deng and Stella Biderman and Sean Welleck},
year={2023},
eprint={2310.10631},
archivePrefix={arXiv},
primaryClass={cs.CL}
}