internlm_-_internlm2-math-plus-7b-gguf

RichardErkhov/internlm_-_internlm2-math-plus-7b-gguf

internlm_-_internlm2-math-plus-7b-gguf is a machine learning model from RichardErkhov. Use it for the machine learning task on the model card, and read the license before you ship it in a product.

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.gguf103 GB · 100%

At a glance

Access: Public
Created: May 28, 2024
Updated: May 28, 2024
SHA: 18e9c711

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From the Hugging Face model README

Quantization made by Richard Erkhov.

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internlm2-math-plus-7b - GGUF

Model creator: https://huggingface.co/internlm/
Original model: https://huggingface.co/internlm/internlm2-math-plus-7b/

Name	Quant method	Size
internlm2-math-plus-7b.Q2_K.gguf	Q2_K	2.8GB
internlm2-math-plus-7b.IQ3_XS.gguf	IQ3_XS	3.1GB
internlm2-math-plus-7b.IQ3_S.gguf	IQ3_S	3.25GB
internlm2-math-plus-7b.Q3_K_S.gguf	Q3_K_S	3.24GB
internlm2-math-plus-7b.IQ3_M.gguf	IQ3_M	3.35GB
internlm2-math-plus-7b.Q3_K.gguf	Q3_K	3.57GB
internlm2-math-plus-7b.Q3_K_M.gguf	Q3_K_M	3.57GB
internlm2-math-plus-7b.Q3_K_L.gguf	Q3_K_L	3.85GB
internlm2-math-plus-7b.IQ4_XS.gguf	IQ4_XS	3.99GB
internlm2-math-plus-7b.Q4_0.gguf	Q4_0	4.15GB
internlm2-math-plus-7b.IQ4_NL.gguf	IQ4_NL	4.19GB
internlm2-math-plus-7b.Q4_K_S.gguf	Q4_K_S	4.18GB
internlm2-math-plus-7b.Q4_K.gguf	Q4_K	4.39GB
internlm2-math-plus-7b.Q4_K_M.gguf	Q4_K_M	4.39GB
internlm2-math-plus-7b.Q4_1.gguf	Q4_1	4.58GB
internlm2-math-plus-7b.Q5_0.gguf	Q5_0	5.0GB
internlm2-math-plus-7b.Q5_K_S.gguf	Q5_K_S	5.0GB
internlm2-math-plus-7b.Q5_K.gguf	Q5_K	5.13GB
internlm2-math-plus-7b.Q5_K_M.gguf	Q5_K_M	5.13GB
internlm2-math-plus-7b.Q5_1.gguf	Q5_1	5.43GB
internlm2-math-plus-7b.Q6_K.gguf	Q6_K	5.91GB
internlm2-math-plus-7b.Q8_0.gguf	Q8_0	7.66GB

Original model description:

pipeline_tag: text-generation license: other language:

en
zh tags:
math

InternLM-Math-Plus

<div align="center"> <img src="https://raw.githubusercontent.com/InternLM/InternLM/main/assets/logo.svg" width="200"/> <div> </div> <div align="center"> <b><font size="5">InternLM-Math</font></b> <sup> <a href="https://internlm.intern-ai.org.cn/"> <i><font size="4">Plus</font></i> </a> </sup> <div> </div> </div>

State-of-the-art bilingual open-sourced Math reasoning LLMs. A solver, prover, verifier, augmentor.

💻 Github 🤗 Demo

</div>

News

[2024.05.24] We release updated version InternLM2-Math-Plus with 4 sizes and state-of-the-art performances including 1.8B, 7B, 20B, and 8x22B. We improve informal math reasoning performance (chain-of-thought and code-intepreter) and formal math reasoning performance (LEAN 4 translation and LEAN 4 theorem proving) significantly.
[2024.02.10] We add tech reports and citation reference.
[2024.01.31] We add MiniF2F results with evaluation codes!
[2024.01.29] We add checkpoints from ModelScope. Update results about majority voting and Code Intepreter. Tech report is on the way!
[2024.01.26] We add checkpoints from OpenXLab, which ease Chinese users to download!

Performance

Formal Math Reasoning

We evaluate the performance of InternLM2-Math-Plus on formal math reasoning benchmark MiniF2F-test. The evaluation setting is same as Llemma with LEAN 4.

Models	MiniF2F-test
ReProver	26.5
LLMStep	27.9
GPT-F	36.6
HTPS	41.0
Llemma-7B	26.2
Llemma-34B	25.8
InternLM2-Math-7B-Base	30.3
InternLM2-Math-20B-Base	29.5
InternLM2-Math-Plus-1.8B	38.9
InternLM2-Math-Plus-7B	43.4
InternLM2-Math-Plus-20B	42.6
InternLM2-Math-Plus-Mixtral8x22B	37.3

Informal Math Reasoning

We evaluate the performance of InternLM2-Math-Plus on informal math reasoning benchmark MATH and GSM8K. InternLM2-Math-Plus-1.8B outperforms MiniCPM-2B in the smallest size setting. InternLM2-Math-Plus-7B outperforms Deepseek-Math-7B-RL which is the state-of-the-art math reasoning open source model. InternLM2-Math-Plus-Mixtral8x22B achieves 68.5 on MATH (with Python) and 91.8 on GSM8K.

Model	MATH	MATH-Python	GSM8K
MiniCPM-2B	10.2	-	53.8
InternLM2-Math-Plus-1.8B	37.0	41.5	58.8
InternLM2-Math-7B	34.6	50.9	78.1
Deepseek-Math-7B-RL	51.7	58.8	88.2
InternLM2-Math-Plus-7B	53.0	59.7	85.8
InternLM2-Math-20B	37.7	54.3	82.6
InternLM2-Math-Plus-20B	53.8	61.8	87.7
Mixtral8x22B-Instruct-v0.1	41.8	-	78.6
Eurux-8x22B-NCA	49.0	-	-
InternLM2-Math-Plus-Mixtral8x22B	58.1	68.5	91.8

We also evaluate models on MathBench-A. InternLM2-Math-Plus-Mixtral8x22B has comparable performance compared to Claude 3 Opus.

Model	Arithmetic	Primary	Middle	High	College	Average
GPT-4o-0513	77.7	87.7	76.3	59.0	54.0	70.9
Claude 3 Opus	85.7	85.0	58.0	42.7	43.7	63.0
Qwen-Max-0428	72.3	86.3	65.0	45.0	27.3	59.2
Qwen-1.5-110B	70.3	82.3	64.0	47.3	28.0	58.4
Deepseek-V2	82.7	89.3	59.0	39.3	29.3	59.9
Llama-3-70B-Instruct	70.3	86.0	53.0	38.7	34.7	56.5
InternLM2-Math-Plus-Mixtral8x22B	77.5	82.0	63.6	50.3	36.8	62.0
InternLM2-Math-20B	58.7	70.0	43.7	24.7	12.7	42.0
InternLM2-Math-Plus-20B	65.8	79.7	59.5	47.6	24.8	55.5
Llama3-8B-Instruct	54.7	71.0	25.0	19.0	14.0	36.7
InternLM2-Math-7B	53.7	67.0	41.3	18.3	8.0	37.7
Deepseek-Math-7B-RL	68.0	83.3	44.3	33.0	23.0	50.3
InternLM2-Math-Plus-7B	61.4	78.3	52.5	40.5	21.7	50.9
MiniCPM-2B	49.3	51.7	18.0	8.7	3.7	26.3
InternLM2-Math-Plus-1.8B	43.0	43.3	25.4	18.9	4.7	27.1

Citation and Tech Report

@misc{ying2024internlmmath,
      title={InternLM-Math: Open Math Large Language Models Toward Verifiable Reasoning}, 
      author={Huaiyuan Ying and Shuo Zhang and Linyang Li and Zhejian Zhou and Yunfan Shao and Zhaoye Fei and Yichuan Ma and Jiawei Hong and Kuikun Liu and Ziyi Wang and Yudong Wang and Zijian Wu and Shuaibin Li and Fengzhe Zhou and Hongwei Liu and Songyang Zhang and Wenwei Zhang and Hang Yan and Xipeng Qiu and Jiayu Wang and Kai Chen and Dahua Lin},
      year={2024},
      eprint={2402.06332},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Created: May 28, 2024
Updated: May 28, 2024