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https://github.com/deepseek-ai/DeepSeek-Coder
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Merge pull request #11 from LyricZhao/main
Fix several rendering problems in README
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README.md
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README.md
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<p align="center">
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<img width="1000px" alt="DeepSeek Coder" src="pictures/logo.png">
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</p>
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<p align="center"><a href="https://www.deepseek.com/">[<img src="pictures/home.png" width="20px"> Homepage]</a> | <a href="https://coder.deepseek.com/">[🤖 Chat with DeepSeek Coder] | <a href="https://huggingface.co/deepseek-ai">[🤗 Models Download]</a> | <a href="https://discord.gg/Tc7c45Zzu5">[Discord]</a> | <a href="https://github.com/guoday/assert/blob/main/QR.png?raw=true">[Wechat(微信)]</a></p>
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<p align="center"><a href="https://www.deepseek.com/">[<img src="pictures/home.png" width="20px"> Homepage]</a> | <a href="https://coder.deepseek.com/">[🤖 Chat with DeepSeek Coder]</a> | <a href="https://huggingface.co/deepseek-ai">[🤗 Models Download]</a> | <a href="https://discord.gg/Tc7c45Zzu5">[Discord]</a> | <a href="https://github.com/guoday/assert/blob/main/QR.png?raw=true">[WeChat (微信)]</a></p>
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<hr>
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@ -38,7 +38,6 @@ And the DeepSeek-Coder-Instruct-33B model after instruction tuning outperforms G
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More evaluation details can be found in the [Detailed Evaluation](#5-detailed-evaluation-results).
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### 3. Procedure of Data Creation and Model Training
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#### Data Creation
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@ -59,8 +58,6 @@ More evaluation details can be found in the [Detailed Evaluation](#5-detailed-ev
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<img src="pictures/model_pretraining.png" alt="model_pretraining" width="100%">
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### 4. How to Use
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Before proceeding, you'll need to install the necessary dependencies. You can do this by running the following command:
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```
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@ -70,7 +67,7 @@ A demo is also available on the [🤗 Hugging Face Space](https://huggingface.co
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Here are some examples of how to use our model.
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#### 1)Code Completion
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#### 1) Code Completion
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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@ -97,7 +94,7 @@ def quick_sort(arr):
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return quick_sort(left) + [pivot] + quick_sort(right)
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```
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#### 2)Code Insertion
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#### 2) Code Insertion
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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@ -124,7 +121,7 @@ This code will output the following result:
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for i in range(1, len(arr)):
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```
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#### 3)Chat Model Inference
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#### 3) Chat Model Inference
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-6.7b-instruct", trust_remote_code=True)
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@ -172,7 +169,7 @@ You are an AI programming assistant, utilizing the Deepseek Coder model, develop
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```
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#### 4)Repository Level Code Completion
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#### 4) Repository Level Code Completion
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-6.7b-base", trust_remote_code=True)
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@ -265,16 +262,16 @@ In the following scenario, the Deepseek-Coder 6.7B model effectively calls a cla
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### 5. Detailed Evaluation Results
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The reproducible code for the following evaluation results can be found in the [Evaluation](https://github.com/deepseek-ai/deepseek-coder/tree/main/Evaluation) directory.
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#### 1)Multilingual HumanEval Benchmark
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#### 1) Multilingual HumanEval Benchmark
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![HumanEval](pictures/HumanEval.png)
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#### 2)MBPP Benchmark
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#### 2) MBPP Benchmark
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<img src="pictures/MBPP.png" alt="MBPP" width="40%">
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#### 3)DS-1000 Benchmark
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#### 3) DS-1000 Benchmark
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![DS-1000](pictures/DS-1000.png)
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#### 4)Program-Aid Math Reasoning Benchmark
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#### 4) Program-Aid Math Reasoning Benchmark
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![Math](pictures/Math.png)
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