mav23/llemma_34b-GGUF

ArXiv | Models | Data | Code | Blog | Sample Explorer

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}
}