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Model: microsoft/rho-math-7b-v0.1 Source: Original Platform
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---
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license: mit
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tags:
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- nlp
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- math
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language:
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- en
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pipeline_tag: text-generation
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---
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<h1 align="center">
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Rho-1: Not All Tokens Are What You Need
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</h1>
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<p align="center">
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<a href="https://arxiv.org/abs/2404.07965"><b>[📜 Arxiv]</b></a> •
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<a href="https://huggingface.co/papers/2404.07965"><b>[💬 HF Paper]</b></a> •
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<a href="https://huggingface.co/microsoft/rho-math-1b-v0.1"><b>[🤗 Models]</b></a> •
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<a href="https://github.com/microsoft/rho"><b>[🐱 GitHub]</b></a>
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</p>
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<p align="center">
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<img src="https://github.com/microsoft/rho/blob/main/docs/static/images/acc_vs_tokens_1b_7b.png?raw=true" width="1000">
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<br>
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<em>Figure 1: Rho-1 is pre-trained with Selective Language Modeling (SLM). SLM improves average few-shot accuracy on GSM8k and MATH by over 16%, achieving the baseline performance 5-10x faster.</em>
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</p>
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## 🔥 News
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- [2024/04/12] 🔥🔥🔥 Rho-Math-v0.1 models released at 🤗 HuggingFace!
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- [Rho-Math-1B](https://huggingface.co/microsoft/rho-math-1b-v0.1) and [Rho-Math-7B](https://huggingface.co/microsoft/rho-math-7b-v0.1) achieve 15.6% and 31.0% few-shot accuracy on MATH dataset, respectively — matching DeepSeekMath with only 3\% of the pretraining tokens.
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- [Rho-Math-1B-Interpreter](https://huggingface.co/microsoft/rho-math-1b-interpreter-v0.1) is the first 1B LLM that achieves over 40% accuracy on MATH.
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- [Rho-Math-7B-Interpreter](https://huggingface.co/microsoft/rho-math-7b-interpreter-v0.1) achieves 52% on MATH dataset, using only 69k samples for fine-tuning.
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- [2024/04/11] Rho-1 paper and repo released.
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## 💡 Introduction
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Rho-1 base models employ Selective Language Modeling (SLM) for pretraining, which selectively trains on clean and useful tokens that aligned with the desired distribution.
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### Selective Lanugage Modeling (SLM)
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<p align="center">
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<img src="https://github.com/microsoft/rho/blob/main/docs/static/images/example.png?raw=true" width="1000">
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<br>
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<em>Figure 2:
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<b>Upper:</b> Even an extensively filtered pretraining corpus contains token-level noise.
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<b>Left:</b> Previous Causal Language Modeling (CLM) trains on all tokens.
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<b>Right:</b> Our proposed Selective Language Modeling (SLM) selectively applies loss on those useful and clean tokens.</em>
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</p>
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<p align="center">
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<img src="https://github.com/microsoft/rho/blob/main/docs/static/images/pipeline.png?raw=true" width="1000">
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<br>
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<em>Figure 3: <b>The pipeline of Selective Language Modeling.</b>
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SLM optimizes language model performance by concentrating on valuable, clean tokens during pre-training.
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It involves three steps:
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(Step 1) Initially, train a reference model on high-quality data.
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(Step 2) Then, score each token's loss in a corpus using the reference model.
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(Step 3) Finally, train the language model selectively on tokens that show higher excess loss compared to the reference loss.</em>
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</p>
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<!-- results: -->
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### Evaluation Results
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Base models (Few-shot CoT):
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| **Model** | **Size** | **Data** | **Uniq. Token** | **Train Token** | **GSM8K** | **MATH** | **MMLU STEM** | **SAT** |
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|:-----------------:|:--------:|:--------:|:---------------:|:---------------:|:---------:|:--------:|:-------------:|:--------:|
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| 1-2B Base Models | | | | | | | | |
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| Qwen1.5 | 1.8B | - | - | - | 36.1 | 6.8 | 31.3 | 40.6 |
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| Gemma | 2.0B | - | - | - | 18.8 | 11.4 | **34.4** | 50.0 |
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| DeepSeekMath | 1.3B | - | 120B | 150B | 23.8 | 13.6 | 33.1 | **56.3** |
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| [Rho-Math-1B-v0.1](https://huggingface.co/microsoft/rho-math-1b-v0.1) | 1.1B | OWM | 14B | 30B | **36.2** | **15.6** | 23.3 | 28.1 |
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| >= 7B Base Models | | | | | | | | |
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| Mistral | 7B | | - | - | 41.2 | 11.6 | 49.5 | 59.4 |
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| Minerva | 540B | - | 39B | 26B | 58.8 | 33.6 | **63.9** | - |
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| LLemma | 34B | PPile | 55B | 50B | 54.2 | 23.0 | 54.7 | 68.8 |
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| InternLM2-Math | 20B | - | 31B | 125B | 65.4 | 30.0 | 53.1 | 71.9 |
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| DeepSeekMath | 7B | - | 120B | 500B | 64.1 | **34.2** | 56.4 | **84.4** |
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| [Rho-Math-7B-v0.1](https://huggingface.co/microsoft/rho-math-7b-v0.1) | 7B | OWM | 14B | 10.5B | **66.9** | 31.0 | 54.6 | **84.4** |
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[Tool-integrated reasoning](https://github.com/microsoft/ToRA) (Code Interpreter):
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| **Model** | **Size** | **SFT Data** | **GSM8k** | **MATH** | **SVAMP** | **ASDiv** | **MAWPS** | **TabMWP** | **GSM-Hard** | **AVG** |
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|------------------------------|----------|--------------|-----------|----------|-----------|-----------|-----------|------------|--------------|----------|
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| gpt4-early (pal) | - | - | 94.2 | 51.8 | 94.8 | 92.6 | 97.7 | 95.9 | 77.6 | 86.4 |
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| gpt-4-turbo-2024-04-09 (cot) | - | - | - | 73.4 | - | - | - | - | - |
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| Open-Source Small Models | | | | | | | | | |
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| MAmmoTH | 70B | MI-260k | 76.9 | 41.8 | 82.4 | - | - | - | - | - |
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| ToRA | 7B | ToRA-69k | 68.8 | 40.1 | 68.2 | 73.9 | 88.8 | 42.4 | 54.6 | 62.4 |
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| ToRA | 70B | ToRA-69k | 84.3 | 49.7 | **82.7** | 86.8 | 93.8 | 74.0 | **67.2** | **76.9** |
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| DeepSeekMath | 7B | ToRA-69k | 79.8 | **52.0** | 80.1 | **87.1** | 93.8 | **85.8** | 63.1 | 77.4 |
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| [Rho-Math-1B-Interpreter-v0.1](https://huggingface.co/microsoft/rho-math-1b-interpreter-v0.1) | 1B | ToRA-69k | 59.4 | 40.6 | 60.7 | 74.2 | 88.6 | 26.7 | 48.1 | 56.9 |
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| [Rho-Math-7B-Interpreter-v0.1](https://huggingface.co/microsoft/rho-math-7b-interpreter-v0.1) | 7B | ToRA-69k | 81.3 | **51.8** | 80.8 | 85.5 | **94.5** | 70.1 | 63.1 | 75.3 |
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## 🚀 Quick Start
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### Evaluation
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```sh
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git clone git@github.com:microsoft/rho.git
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cd rho-1/math-evaluation-harness
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```
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Base model few-shot evaluation:
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```sh
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bash scripts/run_eval.sh cot microsoft/rho-math-7b-v0.1
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```
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SFT model (code-interpreter) evaluation:
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```sh
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bash scripts/run_eval.sh tora microsoft/rho-math-7b-interpreter-v0.1
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```
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Our reproduced outputs are provided in `rho-1/outputs.zip`.
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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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@misc{lin2024rho1,
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title={Rho-1: Not All Tokens Are What You Need},
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author={Zhenghao Lin and Zhibin Gou and Yeyun Gong and Xiao Liu and Yelong Shen and Ruochen Xu and Chen Lin and Yujiu Yang and Jian Jiao and Nan Duan and Weizhu Chen},
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year={2024},
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eprint={2404.07965},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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```
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## Data Summary
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https://huggingface.co/microsoft/rho-math-7b-v0.1/blob/main/data_summary_card.md
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{
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"_name_or_path": "rho-math-7b-v0.1",
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"architectures": [
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"MistralForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 14336,
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"max_position_embeddings": 32768,
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"model_type": "mistral",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
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"rms_norm_eps": 1e-05,
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"rope_theta": 10000.0,
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"sliding_window": 4096,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.38.2",
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"use_cache": true,
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"vocab_size": 32000
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}
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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
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data_summary_card.md
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# Data Summary for microsoft_rho-math-7b-v0.1
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## 1. General information
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**1.0.1 Version of the Summary:** 1.0
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**1.0.2 Last update:** 16-Dec-2025
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## 1.1 Model Developer Identification
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**1.1.1 Model Developer name and contact details:** Microsoft Corporation at One Microsoft Way, Redmond, WA 98052. Tel: 425-882-8080
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## 1.2 Model Identification
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**1.2.1 Versioned model name(s):** Rho-Math-1B-v0.1, Rho-Math-7B-v0.1
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**1.2.2 Model release date:** 12-Apr-2024
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## 1.3 Overall training data size and characteristics
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### 1.3.1 Size of dataset and characteristics
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**1.3.1.A Text training data size:** 1 billion to 1 trillion tokens
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**1.3.1.B Text training data content:** math datasets
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**1.3.1.C Image training data size:** Not applicable. Images are not part of the training data
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**1.3.1.D Image training data content:** Not applicable
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**1.3.1.E Audio training data size:** Not applicable. Audio data is not part of the training data
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**1.3.1.F Audio training data content:** Not applicable
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**1.3.1.G Video training data size:** Not applicable. Audio data is not part of the training data
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**1.3.1.H Video training data content:** Not applicable
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**1.3.1.I Other training data size:** Not applicable
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**1.3.1.J Other training data content:** Not applicable
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**1.3.2 Latest date of data acquisition/collection for model training:** 12-Apr-2024
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**1.3.3 Is data collection ongoing to update the model with new data collection after deployment?** No
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**1.3.4 Date the training dataset was first used to train the model:** 1-Jan-2024
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**1.3.5 Rationale or purpose of data selection:** Datasets were chosen to focus pretraining on clean, useful math and general-language tokens aligned to desired distributions for improved reasoning and efficiency.
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## 2. List of data sources
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### 2.1 Publicly available datasets
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**2.1.1 Have you used publicly available datasets to train the model?** Yes
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## 2.2 Private non-publicly available datasets obtained from third parties
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### 2.2.1 Datasets commercially licensed by rights holders or their representatives
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**2.2.1.A Have you concluded transactional commercial licensing agreement(s) with rights holder(s) or with their representatives?** Not applicable
|
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|
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### 2.2.2 Private datasets obtained from other third-parties
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**2.2.2.A Have you obtained private datasets from third parties that are not licensed as described in Section 2.2.1, such as data obtained from providers of private databases, or data intermediaries?** No
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## 2.3 Personal Information
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**2.3.1 Was personal data used to train the model?** Microsoft follows all relevant laws and regulations pertaining to personal information
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## 2.4 Synthetic data
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**2.4.1 Was any synthetic AI-generated data used to train the model?** Yes
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## 3. Data processing aspects
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### 3.1 Respect of reservation of rights from text and data mining exception or limitation
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**3.1.1 Does this dataset include any data protected by copyright, trademark, or patent?** Microsoft follows all required regulations and laws for processing data protected by copyright, trademark, or patent
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## 3.2 Other information
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**3.2.1 Does the dataset include information about consumer groups without revealing individual consumer identities?** Microsoft follows all required regulations and laws for protecting consumer identities
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**3.2.2 Was the dataset cleaned or modified before model training?** Yes
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{
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"_from_model_config": true,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"transformers_version": "4.38.2"
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}
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|
||||
}
|
||||
}
|
||||
23
special_tokens_map.json
Normal file
23
special_tokens_map.json
Normal file
@@ -0,0 +1,23 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "</s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"unk_token": {
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
Binary file not shown.
BIN
tokenizer.model
(Stored with Git LFS)
Normal file
BIN
tokenizer.model
(Stored with Git LFS)
Normal file
Binary file not shown.
42
tokenizer_config.json
Normal file
42
tokenizer_config.json
Normal file
@@ -0,0 +1,42 @@
|
||||
{
|
||||
"add_bos_token": true,
|
||||
"add_eos_token": false,
|
||||
"added_tokens_decoder": {
|
||||
"0": {
|
||||
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|
||||
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|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"1": {
|
||||
"content": "<s>",
|
||||
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|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"2": {
|
||||
"content": "</s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [],
|
||||
"bos_token": "<s>",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "</s>",
|
||||
"legacy": true,
|
||||
"model_max_length": 1000000000000000019884624838656,
|
||||
"pad_token": null,
|
||||
"sp_model_kwargs": {},
|
||||
"spaces_between_special_tokens": false,
|
||||
"tokenizer_class": "LlamaTokenizer",
|
||||
"unk_token": "<unk>",
|
||||
"use_default_system_prompt": false
|
||||
}
|
||||
Reference in New Issue
Block a user