Text Generation
Transformers
Safetensors
English
mistral
text-generation-inference
Inference Endpoints
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- library_name: transformers
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- tags: []
 
 
 
 
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  ---
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- # Model Card for Model ID
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- <!-- Provide a quick summary of what the model is/does. -->
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- ## Model Details
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- ### Model Description
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- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- ## Uses
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- ### Direct Use
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- ## Bias, Risks, and Limitations
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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- ### Recommendations
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- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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- ## How to Get Started with the Model
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- Use the code below to get started with the model.
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- [More Information Needed]
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- ## Training Details
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- ### Training Data
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- <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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- ### Training Procedure
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- #### Preprocessing [optional]
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- #### Training Hyperparameters
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- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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  ## Evaluation
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- <!-- This section describes the evaluation protocols and provides the results. -->
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
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- #### Factors
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- #### Metrics
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- ### Results
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- #### Summary
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- ## Model Examination [optional]
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- ## Environmental Impact
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- Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- ## Technical Specifications [optional]
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- ## Glossary [optional]
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- ## More Information [optional]
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- ## Model Card Authors [optional]
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- ## Model Card Contact
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  ---
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+ datasets:
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+ - ToheartZhang/JiuZhang3.0-Corpus-PT-CoT
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+ - ToheartZhang/JiuZhang3.0-Corpus-PT-Tool
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+ - ToheartZhang/JiuZhang3.0-Corpus-SFT
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+ language:
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+ - en
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  ---
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+ <h1 align="center">
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+ JiuZhang3.0: Efficiently Improving Mathematical
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+ Reasoning by Training Small Data Synthesis Models
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+ </h1>
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+ <p align="center">
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+ <a href="https://arxiv.org/abs/2405.14365"><b>[Paper]</b></a> •
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+ <a href="https://github.com/RUCAIBox/JiuZhang3.0"><b>[GitHub]</b></a> •
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+ <a href="https://huggingface.co/collections/ToheartZhang/jiuzhang30-66508be8be5a61de47101655#/"><b>[Models]</b></a>
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+ <a href="https://huggingface.co/collections/ToheartZhang/jiuzhang30-corpus-665092209525389ad7a2289a"><b>[Data]</b></a>
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+ </p>
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+
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+ ## Introduction
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+ JiuZhang3.0 is a series of fine-tuned models for math reasoning continually pre-trained on corpus synthesized by our carefully trained small LLM.
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+
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+ ## Experimental Results
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+ For more evaluation results, please refer to the [Paper](https://arxiv.org/abs/2405.14365)
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+ | Models | GSM8k | MATH | SVAMP | ASDiv | MAWPS | CARP | Avg. |
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+ |--------------------------|-------|------|-------|-------|-------|------|-------|
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+ | GPT-4 | 92.2 | 65.4 | 92.9 | 94.3 | 96.6 | 53.6 | 82.5 |
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+ |**20B+ Models**||
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+ | Llemma-34B | 60.2 | 24.6 | 68.0 | 75.6 | 89.8 | 36.5 | 59.1 |
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+ | Intern-Math-20B | 64.9 | 27.4 | 74.9 | 79.6 | 94.4 | 42.3 | 63.9 |
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+ | ChatGLM-Math-32B | 82.6 | 40.6 | - | - | - | - | - |
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+ | MAmmoTH2-8x7B-Plus | _86.4_| 47.0 | _90.0_| _92.2_| **97.0** | 45.8 | _76.4_ |
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+ | [JiuZhang3.0-8x7B](https://huggingface.co/ToheartZhang/JiuZhang3.0-8x7B) | **89.8** | **53.8** | **90.2** | **93.1** | _96.7_ | 52.3 | **79.3** |
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+ |**7-8B Models**||
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+ | Mistral-7B-MMIQC | 75.0 | 34.2 | 73.5 | 82.1 | 90.1 | 36.5 | 65.2 |
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+ | MetaMath-Mistral-7B | 77.8 | 29.6 | 79.6 | 81.2 | 93.7 | 30.5 | 65.4 |
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+ | Abel-7B-002 | 80.4 | 29.6 | 78.8 | 82.7 | 93.5 | 33.2 | 66.4 |
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+ | WizardMath-7B-1.1 | 82.2 | 32.8 | 80.7 | 84.2 | 93.8 | 31.9 | 67.6 |
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+ | Math-Shepherd-Mistral-7B | 84.3 | 34.4 | 82.9 | 82.8 | 92.5 | 32.9 | 68.3 |
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+ | KPMath-DSMath-7B | 83.9 | 48.8 | 81.5 | 88.9 | 94.8 | - | - |
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+ | MAmmoTH2-7B-Plus | 84.2 | 46.2 | _90.3_| 90.3 | _97.1_| 44.3 | 75.2 |
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+ | MAmmoTH2-8B-Plus | 84.4 | 41.2 | 89.9 | 89.9 | _97.1_| 44.8 | 74.6 |
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+ | DeepSeekMath-7B-Instruct | 82.3 | 45.8 | 83.7 | 90.1 | 95.7 | 45.8 | 73.9 |
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+ | DeepSeekMath-7B-RL | 88.2 | 50.2 | 87.3 | 91.8 | 95.5 | **51.6** | 77.4 |
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+ | [JiuZhang3.0-7B](https://huggingface.co/ToheartZhang/JiuZhang3.0-7B) | **88.6** | **52.8** | **90.4** | **92.6** | **97.3** | _51.0_ | **78.8** |
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+ | [JiuZhang3.0-8B](https://huggingface.co/ToheartZhang/JiuZhang3.0-8B) | **88.6** | _51.0_ | 89.4 | **92.6** | _97.1_ | 50.9 | _78.3_ |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ## Evaluation
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+ ### Natural Language Reasoning
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+ ```
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+ ## Question
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+ {question}
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+
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+ ## Solution
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+ {solution}
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+ ```
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+
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+ ### Tool Manipulation
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+ ```
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+ ## Question
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+ {question}
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+
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+ ## Code Solution
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+ {solution}
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+ ```
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+
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+ ## Citation
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+ If you find this repository helpful, please consider citing our paper:
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+
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+ ```
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+ @article{zhou2024jiuzhang30,
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+ title={JiuZhang3.0: Efficiently Improving Mathematical Reasoning by Training Small Data Synthesis Models},
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+ author={Kun Zhou and Beichen Zhang and Jiapeng Wang and Zhipeng Chen and Wayne Xin Zhao and Jing Sha and Zhichao Sheng and Shijin Wang and Ji-Rong Wen},
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+ year={2024},
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+ }
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+ ```