Model Download |
Evaluation Results |
Quick Start |
License |
Citation
Paper Link👁️
## 1. Introduction
DeepSeekMath is initialized with [DeepSeek-Coder-v1.5 7B](https://huggingface.co/deepseek-ai/deepseek-coder-7b-base-v1.5) and continues pre-training on math-related tokens sourced from Common Crawl, together with natural language and code data for 500B tokens. DeepSeekMath 7B has achieved an impressive score of **51.7%** on the competition-level MATH benchmark without relying on external toolkits and voting techniques, approaching the performance level of Gemini-Ultra and GPT-4. For research purposes, we release [checkpoints](#4-model-downloads) of base, instruct, and RL models to the public.
## 2. Evaluation Results
### DeepSeekMath-Base 7B
We conduct a comprehensive assessment of the mathematical capabilities of DeepSeekMath-Base 7B, focusing on its ability to produce self-contained mathematical solutions without relying on external tools, solve math problems using tools, and conduct formal theorem proving. Beyond mathematics, we also provide a more general profile of the base model, including its performance of natural language understanding, reasoning, and programming skills.
- **Mathematical problem solving with step-by-step reasoning**
- **Mathematical problem solving with tool use**
- **Natural Language Understanding, Reasoning, and Code**
The evaluation results from the tables above can be summarized as follows:
- **Superior Mathematical Reasoning:** On the competition-level MATH dataset, DeepSeekMath-Base 7B outperforms existing open-source base models by more than 10% in absolute terms through few-shot chain-of-thought prompting, and also surpasses Minerva 540B.
- **Strong Tool Use Ability:** Continuing pre-training with DeepSeekCoder-Base-7B-v1.5 enables DeepSeekMath-Base 7B to more effectively solve and prove mathematical problems by writing programs.
- **Comparable Reasoning and Coding Performance:** DeepSeekMath-Base 7B achieves performance in reasoning and coding that is comparable to that of DeepSeekCoder-Base-7B-v1.5.
### DeepSeekMath-Instruct and -RL 7B
DeepSeekMath-Instruct 7B is a mathematically instructed tuning model derived from DeepSeekMath-Base 7B, while DeepSeekMath-RL 7B is trained on the foundation of DeepSeekMath-Instruct 7B, utilizing our proposed Group Relative Policy Optimization (GRPO) algorithm.
We evaluate mathematical performance both without and with tool use, on 4 quantitative reasoning benchmarks in English and Chinese. As shown in Table, DeepSeekMath-Instruct 7B demonstrates strong performance of step-by-step reasoning, and DeepSeekMath-RL 7B approaches an accuracy of 60% on MATH with tool use, surpassing all existing open-source models.
## 3. Data Collection
- Step 1: Select [OpenWebMath](https://arxiv.org/pdf/2310.06786.pdf), a collection of high-quality mathematical web texts, as our initial seed corpus for training a FastText model.
- Step 2: Use the FastText model to retrieve mathematical web pages from the deduplicated Common Crawl database.
- Step 3: Identify potential math-related domains through statistical analysis.
- Step 4: Manually annotate URLs within these identified domains that are associated with mathematical content.
- Step 5: Add web pages linked to these annotated URLs, but not yet collected, to the seed corpus. Jump to step 1 until four iterations.
After four iterations of data collection, we end up with **35.5M** mathematical web pages, totaling **120B** tokens.
## 4. Model Downloads
We release the DeepSeekMath 7B, including base, instruct and RL models, to the public. To support a broader and more diverse range of research within both academic and commercial communities. Please **note** that the use of this model is subject to the terms outlined in [License section](#6-license). Commercial usage is permitted under these terms.
### Huggingface
| Model | Sequence Length | Download |
| :----------------------- | :-------------: | :----------------------------------------------------------: |
| DeepSeekMath-Base 7B | 4096 | 🤗 [HuggingFace](https://huggingface.co/deepseek-ai/deepseek-math-7b-base) |
| DeepSeekMath-Instruct 7B | 4096 | 🤗 [HuggingFace](https://huggingface.co/deepseek-ai/deepseek-math-7b-instruct) |
| DeepSeekMath-RL 7B | 4096 | 🤗 [HuggingFace](https://huggingface.co/deepseek-ai/deepseek-math-7b-rl) |
## 5. Quick Start
You can directly employ [Huggingface's Transformers](https://github.com/huggingface/transformers) for model inference.
**Text Completion**
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
model_name = "deepseek-ai/deepseek-math-7b-base"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
model.generation_config = GenerationConfig.from_pretrained(model_name)
model.generation_config.pad_token_id = model.generation_config.eos_token_id
text = "The integral of x^2 from 0 to 2 is"
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs.to(model.device), max_new_tokens=100)
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(result)
```
**Chat Completion**
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
model_name = "deepseek-ai/deepseek-math-7b-instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
model.generation_config = GenerationConfig.from_pretrained(model_name)
model.generation_config.pad_token_id = model.generation_config.eos_token_id
messages = [
{"role": "user", "content": "what is the integral of x^2 from 0 to 2?"}
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
outputs = model.generate(input_tensor.to(model.device), max_new_tokens=100)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)
print(result)
```
Avoiding the use of the provided function `apply_chat_template`, you can also interact with our model following the sample template. Note that `messages` should be replaced by your input.
```
User: {messages[0]['content']}
Assistant: {messages[1]['content']}<｜end▁of▁sentence｜>User: {messages[2]['content']}
Assistant:
```
**Note:** By default (`add_special_tokens=True`), our tokenizer automatically adds a `bos_token` (`<｜begin▁of▁sentence｜>`) before the input text. Additionally, since the system prompt is not compatible with this version of our models, we DO NOT RECOMMEND including the system prompt in your input.
❗❗❗ **Please use chain-of-thought prompt to test DeepSeekMath-Instruct and DeepSeekMath-RL:**
- English questions: **{question}\nPlease reason step by step, and put your final answer within \\boxed{}.**
- Chinese questions: **{question}\n请通过逐步推理来解答问题，并把最终答案放置于\\boxed{}中。**
## 6. License
This code repository is licensed under the MIT License. The use of DeepSeekMath models is subject to the Model License. DeepSeekMath supports commercial use.
See the [LICENSE-CODE](LICENSE-CODE) and [LICENSE-MODEL](LICENSE-MODEL) for more details.
## 7. Citation
```
@misc{deepseek-math,
author = {Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Mingchuan Zhang, Y.K. Li, Y. Wu, Daya Guo},
title = {DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models},
journal = {CoRR},
volume = {abs/2402.03300},
year = {2024},
url = {https://arxiv.org/abs/2402.03300},
}
```
## 8. Contact
If you have any questions, please raise an issue or contact us at [service@deepseek.com](mailto:service@deepseek.com).
