Skip to content

Update README.md #50

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
kinngg2 wants to merge 1 commit into
base: main
Choose a base branch
from
Open

Update README.md #50

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
202 changes: 0 additions & 202 deletions README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -11,208 +11,6 @@

<a href="https://www.deepseek.com/" target="_blank">
<img alt="Homepage" src="images/badge.svg" />
</a>
<a href="https://chat.deepseek.com/" target="_blank">
<img alt="Chat" src="https://img.shields.io/badge/🤖%20Chat-DeepSeek%20LLM-536af5?color=536af5&logoColor=white" />
</a>
<a href="https://huggingface.co/deepseek-ai" target="_blank">
<img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-DeepSeek%20AI-ffc107?color=ffc107&logoColor=white" />
</a>
<a href="https://replicate.com/cjwbw/deepseek-math-7b-base" target="_parent"><img src="https://replicate.com/cjwbw/deepseek-math-7b-base/badge" alt="Replicate"/></a>
</div>

<div align="center">

<a href="https://discord.gg/Tc7c45Zzu5" target="_blank">
<img alt="Discord" src="https://img.shields.io/badge/Discord-DeepSeek%20AI-7289da?logo=discord&logoColor=white&color=7289da" />
</a>
<a href="images/qr.jpeg" target="_blank">
<img alt="Wechat" src="https://img.shields.io/badge/WeChat-DeepSeek%20AI-brightgreen?logo=wechat&logoColor=white" />
</a>
<a href="https://twitter.com/deepseek_ai" target="_blank">
<img alt="Twitter Follow" src="https://img.shields.io/badge/Twitter-deepseek_ai-white?logo=x&logoColor=white" />
</a>

</div>

<div align="center">

<a href="LICENSE-CODE">
<img alt="Code License" src="https://img.shields.io/badge/Code_License-MIT-f5de53?&color=f5de53">
</a>
<a href="LICENSE-MODEL">
<img alt="Model License" src="https://img.shields.io/badge/Model_License-Model_Agreement-f5de53?&color=f5de53">
</a>
</div>


<p align="center">
<a href="#4-model-downloads">Model Download</a> |
<a href="#2-evaluation-results">Evaluation Results</a> |
<a href="#5-quick-start">Quick Start</a> |
<a href="#6-license">License</a> |
<a href="#7-citation">Citation</a>
</p>

<p align="center">
<a href="https://arxiv.org/pdf/2402.03300.pdf"><b>Paper Link</b>👁️</a>
</p>


## 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.

<p align="center">
<img src="images/math.png" alt="table" width="70%">
</p>

## 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**

<p align="center">
<img src="images/base_results_1.png" alt="table" width="70%">
</p>

- **Mathematical problem solving with tool use**

<p align="center">
<img src="images/base_results_2.png" alt="table" width="50%">
</p>

- **Natural Language Understanding, Reasoning, and Code**
<p align="center">
<img src="images/base_results_3.png" alt="table" width="50%">
</p>

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.

<p align="center">
<img src="images/instruct_results.png" alt="table" width="50%">
</p>


## 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.


<p align="center">
<img src="images/data_pipeline.png" alt="table" width="80%">
</p>

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?\nPlease reason step by step, and put your final answer within \boxed{}."}
]
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},
}
```

Expand Down