275 lines
12 KiB
Markdown
275 lines
12 KiB
Markdown
<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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<hr>
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### 1. Introduction of DeepSeek Coder
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Deepseek Coder comprises a series of code language models trained on both 87% code and 13% natural language in English and Chinese, with each model pre-trained on 2T tokens. We provide various sizes of the code model, ranging from 1B to 33B versions. Each model is pre-trained on project-level code corpus by employing a window size of 16K and a extra fill-in-the-blank task, to support project-level code completion and infilling. For coding capabilities, Deepseek Coder achieves state-of-the-art performance among open-source code models on multiple programming languages and various benchmarks.
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<p align="center">
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<img src="pictures/result.png" alt="result" width="70%">
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</p>
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- **Massive Training Data**: Trained on 2T tokens, including 87% code and 13% linguistic data in both English and Chinese languages.
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- **Highly Flexible & Scalable**: Offered in model sizes of 1B, 5.7B, 6.7B and 33B, enabling users to choose the setup most suitable for their requirements.
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- **Superior Model Performance**: State-of-the-art performance among publicly available code models on HumanEval, MultiPL-E, MBPP, DS-1000, and APPS benchmarks.
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- **Advanced Code Completion Capabilities**: A window size of 16K and a fill-in-the-blank task, supporting project-level code completion and infilling tasks.
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### 2. Evaluation Results
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We evaluate DeepSeek Coder on various coding-related benchmarks.
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Only `pass@1` results on HumanEval (Python and Multilingual), MBPP, DS-1000 are reported here:
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<p align="center">
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<img src="pictures/table.png" alt="table" width="70%">
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</p>
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The result shows that DeepSeek-Coder-Base-33B significantly outperforms existing open-source code LLMs. Compared with CodeLlama-34B, it leads by 7.9%, 9.3%, 10.8% and 5.9% respectively on HumanEval Python, HumanEval Multilingual, MBPP and DS-1000.
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Surprisingly, our DeepSeek-Coder-Base-7B reaches the performance of CodeLlama-34B.
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And the DeepSeek-Coder-Instruct-33B model after instruction tuning outperforms GPT35-turbo on HumanEval and achieves comparable result with GPT35-turbo on MBPP.
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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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- Step 1: Collecting code data from GitHub and apply the same filtering rules as [StarcoderData](https://github.com/bigcode-project/bigcode-dataset) to filter data.
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- Step 2: Parsing the dependencies of files within the same repository to rearrange the file positions based on their dependencies.
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- Step 3: Concatenating dependent files to form a single example and employ repo-level minhash for deduplication.
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- Step 4: Further filtering out low-quality code, such as codes with syntax errors or poor readability.
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<img src="pictures/data_clean.png" alt="data_creation" width="100%">
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#### Model Training
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- Step 1: Initially pre-trained with a dataset consisting of 87% code, 10% code-related language (Github Markdown and StackExchange), and 3% non-code related Chinese language. Models are pre-trained using 1.8T tokens and a 4K window size in this step.
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- Step 2: Further Pre-training using an extended 16K window size on an additional 200B tokens, resulting in foundational models (**DeepSeek-Coder-Base**).
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- Step 3: Instruction Fine-tuning on 2B tokens of instruction data, resulting in instruction-tuned models (**DeepSeek-Coder-Instruct**).
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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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pip install -r requirements.txt
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```
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A demo is also available on the [🤗 Hugging Face Space](https://huggingface.co/spaces/deepseek-ai/deepseek-coder-7b-instruct), and you can run the demo locally using `app.py` in [demo](https://github.com/deepseek-ai/deepseek-coder/tree/main/demo) folder. (Thanks to all the HF team for their support)
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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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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-6.7b-base", trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-6.7b-base", trust_remote_code=True).cuda()
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input_text = "#write a quick sort algorithm"
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inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
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outputs = model.generate(**inputs, max_length=128)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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This code will output the following result:
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```
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def quick_sort(arr):
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if len(arr) <= 1:
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return arr
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pivot = arr[0]
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left = []
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right = []
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for i in range(1, len(arr)):
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if arr[i] < pivot:
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left.append(arr[i])
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else:
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right.append(arr[i])
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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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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-6.7b-base", trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-6.7b-base", trust_remote_code=True).cuda()
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input_text = """<｜fim▁begin｜>def quick_sort(arr):
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if len(arr) <= 1:
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return arr
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pivot = arr[0]
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left = []
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right = []
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<｜fim▁hole｜>
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if arr[i] < pivot:
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left.append(arr[i])
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else:
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right.append(arr[i])
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return quick_sort(left) + [pivot] + quick_sort(right)<｜fim▁end｜>"""
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inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
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outputs = model.generate(**inputs, max_length=128)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)[len(input_text):])
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```
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This code will output the following result:
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```
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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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```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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model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-6.7b-instruct", trust_remote_code=True).cuda()
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messages=[
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{ 'role': 'user', 'content': "write a quick sort algorithm in python."}
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]
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inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
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# 32021 is the id of <|EOT|> token
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outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, top_k=50, top_p=0.95, num_return_sequences=1, eos_token_id=32021)
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print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))
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```
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This code will output the following result:
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```
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Sure, here is a simple implementation of the Quick Sort algorithm in Python:
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def quick_sort(arr):
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if len(arr) <= 1:
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return arr
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else:
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pivot = arr[0]
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less_than_pivot = [x for x in arr[1:] if x <= pivot]
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greater_than_pivot = [x for x in arr[1:] if x > pivot]
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return quick_sort(less_than_pivot) + [pivot] + quick_sort(greater_than_pivot)
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# Test the function
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arr = [10, 7, 8, 9, 1, 5]
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print("Original array:", arr)
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print("Sorted array:", quick_sort(arr))
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This code works by selecting a 'pivot' element from the array and partitioning the other elements into two sub-arrays, according to whether they are less than or greater than the pivot. The pivot element is then in its final position. The process is then repeated for the sub-arrays.
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```
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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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model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-6.7b-base", trust_remote_code=True).cuda()
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input_text = """#utils.py
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import torch
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from sklearn import datasets
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from sklearn.model_selection import train_test_split
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from sklearn.preprocessing import StandardScaler
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from sklearn.metrics import accuracy_score
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def load_data():
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iris = datasets.load_iris()
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X = iris.data
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y = iris.target
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# Standardize the data
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scaler = StandardScaler()
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X = scaler.fit_transform(X)
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
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# Convert numpy data to PyTorch tensors
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X_train = torch.tensor(X_train, dtype=torch.float32)
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X_test = torch.tensor(X_test, dtype=torch.float32)
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y_train = torch.tensor(y_train, dtype=torch.int64)
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y_test = torch.tensor(y_test, dtype=torch.int64)
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return X_train, X_test, y_train, y_test
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def evaluate_predictions(y_test, y_pred):
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return accuracy_score(y_test, y_pred)
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#model.py
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import torch
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import torch.nn as nn
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import torch.optim as optim
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from torch.utils.data import DataLoader, TensorDataset
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class IrisClassifier(nn.Module):
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def __init__(self):
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super(IrisClassifier, self).__init__()
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self.fc = nn.Sequential(
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nn.Linear(4, 16),
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nn.ReLU(),
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nn.Linear(16, 3)
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)
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def forward(self, x):
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return self.fc(x)
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def train_model(self, X_train, y_train, epochs, lr, batch_size):
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criterion = nn.CrossEntropyLoss()
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optimizer = optim.Adam(self.parameters(), lr=lr)
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# Create DataLoader for batches
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dataset = TensorDataset(X_train, y_train)
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dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)
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for epoch in range(epochs):
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for batch_X, batch_y in dataloader:
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optimizer.zero_grad()
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outputs = self(batch_X)
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loss = criterion(outputs, batch_y)
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loss.backward()
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optimizer.step()
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def predict(self, X_test):
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with torch.no_grad():
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outputs = self(X_test)
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_, predicted = outputs.max(1)
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return predicted.numpy()
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#main.py
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from utils import load_data, evaluate_predictions
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from model import IrisClassifier as Classifier
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def main():
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# Model training and evaluation
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"""
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inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=140)
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print(tokenizer.decode(outputs[0]))
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```
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---
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In the following scenario, the Deepseek-Coder 6.7B model effectively calls a class **IrisClassifier** and its member function from the `model.py` file, and also utilizes functions from the `utils.py` file, to correctly complete the **main** function in`main.py` file for model training and evaluation.
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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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#### 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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#### 4）Program-Aid Math Reasoning Benchmark
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### 6. License
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This code repository is licensed under the MIT License. The use of DeepSeek Coder models is subject to the Model License. DeepSeek Coder supports commercial use.
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See the [LICENSE-CODE](LICENSE-CODE) and [LICENSE-MODEL](LICENSE-MODEL) for more details.
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### 6. Contact
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If you have any questions, please raise an issue or contact us at [agi_code@deepseek.com](mailto:agi_code@deepseek.com).
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