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            license: other
         
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            license_name: deepseek
         
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            license_link: LICENSE
         
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            ---
         
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            license: other
         
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            license_name: deepseek
         
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            license_link: https://github.com/deepseek-ai/DeepSeek-Math/blob/main/LICENSE-MODEL
         
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            ---
         
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            <p align="center">
         
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            <img width="500px" alt="DeepSeek Chat" src="https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/images/logo.png?raw=true">
         
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            </p>
         
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            <p align="center"><a href="https://www.deepseek.com/">[🏠Homepage]</a>  |  <a href="https://chat.deepseek.com/">[🤖 Chat with DeepSeek LLM]</a>  |  <a href="https://discord.gg/Tc7c45Zzu5">[Discord]</a>  |  <a href="https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/images/qr.jpeg">[Wechat(微信)]</a> </p>
         
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            <p align="center">
         
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              <a href="https://arxiv.org/pdf/xxx.pdf"><b>Paper Link</b>👁️</a>
         
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            </p>
         
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            <hr>
         
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            ### 1. Introduction to DeepSeekMath
         
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            See the [Introduction](https://github.com/deepseek-ai/DeepSeek-Math) for more details.
         
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            ### 2. How to Use
         
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            Here give some examples of how to use our model.
         
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            **Text Completion**
         
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            ```python
         
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            import torch
         
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            from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
         
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            model_name = "deepseek-ai/deepseek-math-7b-base"
         
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            tokenizer = AutoTokenizer.from_pretrained(model_name)
         
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            model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
         
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            model.generation_config = GenerationConfig.from_pretrained(model_name)
         
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            model.generation_config.pad_token_id = model.generation_config.eos_token_id
         
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            text = "The integral of x^2 from 0 to 2 is"
         
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            inputs = tokenizer(text, return_tensors="pt")
         
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            outputs = model.generate(**inputs.to(model.device), max_new_tokens=100)
         
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            result = tokenizer.decode(outputs[0], skip_special_tokens=True)
         
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            print(result)
         
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            ```
         
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            ### 3. License
         
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            This code repository is licensed under the MIT License. The use of DeepSeekMath models is subject to the Model License. DeepSeekMath supports commercial use.
         
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            See the [LICENSE-MODEL](https://github.com/deepseek-ai/DeepSeek-Math/blob/main/LICENSE-MODEL) for more details.
         
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            ### 4. Contact
         
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            If you have any questions, please raise an issue or contact us at [[email protected]](mailto:[email protected]).
         
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