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@ -69,7 +69,7 @@ A demo is also available on the [🤗 Hugging Face Space](https://huggingface.co
Here are some examples of how to use our model. Here are some examples of how to use our model.
#### 1）Code Completion #### 1) Code Completion
```python ```python
from transformers import AutoTokenizer, AutoModelForCausalLM from transformers import AutoTokenizer, AutoModelForCausalLM
import torch import torch
@ -96,7 +96,7 @@ def quick_sort(arr):
return quick_sort(left) + [pivot] + quick_sort(right) return quick_sort(left) + [pivot] + quick_sort(right)
``` ```
#### 2）Code Insertion #### 2) Code Insertion
```python ```python
from transformers import AutoTokenizer, AutoModelForCausalLM from transformers import AutoTokenizer, AutoModelForCausalLM
import torch import torch
@ -123,7 +123,7 @@ This code will output the following result:
for i in range(1, len(arr)): for i in range(1, len(arr)):
``` ```
#### 3）Chat Model Inference #### 3) Chat Model Inference
```python ```python
from transformers import AutoTokenizer, AutoModelForCausalLM from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-6.7b-instruct", trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-6.7b-instruct", trust_remote_code=True)
@ -171,7 +171,7 @@ You are an AI programming assistant, utilizing the Deepseek Coder model, develop
``` ```
#### 4）Repository Level Code Completion #### 4) Repository Level Code Completion
```python ```python
from transformers import AutoTokenizer, AutoModelForCausalLM from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-6.7b-base", trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-6.7b-base", trust_remote_code=True)
@ -264,16 +264,16 @@ In the following scenario, the Deepseek-Coder 6.7B model effectively calls a cla
### 5. Detailed Evaluation Results ### 5. Detailed Evaluation Results
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. 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.
#### 1）Multilingual HumanEval Benchmark #### 1) Multilingual HumanEval Benchmark
![HumanEval](pictures/HumanEval.png) ![HumanEval](pictures/HumanEval.png)
#### 2）MBPP Benchmark #### 2) MBPP Benchmark
<img src="pictures/MBPP.png" alt="MBPP" width="40%"> <img src="pictures/MBPP.png" alt="MBPP" width="40%">
#### 3）DS-1000 Benchmark #### 3) DS-1000 Benchmark
![DS-1000](pictures/DS-1000.png) ![DS-1000](pictures/DS-1000.png)
#### 4）Program-Aid Math Reasoning Benchmark #### 4) Program-Aid Math Reasoning Benchmark
![Math](pictures/Math.png) ![Math](pictures/Math.png)