diff --git a/README.md b/README.md index e96f1f5..de324df 100644 --- a/README.md +++ b/README.md @@ -11,208 +11,6 @@ Homepage - - - Chat - - - Hugging Face - - Replicate - - -
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- Model Download | - Evaluation Results | - Quick Start | - License | - Citation -

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- 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. - -

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- -## 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** - -

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- -- **Mathematical problem solving with tool use** - -

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- -- **Natural Language Understanding, Reasoning, and Code** -

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- -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. - -

-table -

- - -## 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. - - -

-table -

- -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}, } ```