--- license: other license_name: deepseek license_link: LICENSE --- ### 1. Introduction of Deepseek Coder 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. - **Massive Training Data**: Trained on 2T tokens, including 87% code and 13% linguistic data in both English and Chinese languages. - **Highly Flexible & Scalable**: Offered in model sizes of 1B, 7B, and 33B, enabling users to choose the setup most suitable for their requirements. - **Superior Model Performance**: State-of-the-art performance among publicly available code models on HumanEval, MultiPL-E, MBPP, DS-1000, and APPS benchmarks. - **Advanced Code Completion Capabilities**: A window size of 16K and a fill-in-the-blank task, supporting project-level code completion and infilling tasks. ### 2. Model Summary deepseek-coder-5.7bmqa-base is a 5.7B parameter model with Multi Query Attention trained on 2 trillion tokens. - **Home Page:** [DeepSeek](https://deepseek.com/) - **Repository:** [deepseek-ai/deepseek-coder](https://github.com/deepseek-ai/deepseek-coder) - **Chat With DeepSeek Coder:** [DeepSeek-Coder](https://coder.deepseek.com/) ### 3. How to Use Here give some examples of how to use our model. #### 1）Code Completion ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-5.7bmqa-base", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-5.7bmqa-base", trust_remote_code=True).cuda() input_text = "#write a quick sort algorithm" inputs = tokenizer(input_text, return_tensors="pt").cuda() outputs = model.generate(**inputs, max_length=128) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` This code will output the following result: ``` def quick_sort(arr): if len(arr) <= 1: return arr pivot = arr[0] left = [] right = [] for i in range(1, len(arr)): if arr[i] < pivot: left.append(arr[i]) else: right.append(arr[i]) return quick_sort(left) + [pivot] + quick_sort(right) ``` #### 2）Code Insertion ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-5.7bmqa-base", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-5.7bmqa-base", trust_remote_code=True).cuda() input_text = """def quick_sort(arr): if len(arr) <= 1: return arr pivot = arr[0] left = [] right = [] if arr[i] < pivot: left.append(arr[i]) else: right.append(arr[i]) return quick_sort(left) + [pivot] + quick_sort(right)""" inputs = tokenizer(input_text, return_tensors="pt").cuda() outputs = model.generate(**inputs, max_length=128) print(tokenizer.decode(outputs[0], skip_special_tokens=True)[len(input_text):]) ``` This code will output the following result: ``` for i in range(1, len(arr)): ``` #### 3）Repository Level Code Completion ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-5.7bmqa-base", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-5.7bmqa-base", trust_remote_code=True).cuda() input_text = """#utils.py import torch from sklearn import datasets from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.metrics import accuracy_score def load_data(): iris = datasets.load_iris() X = iris.data y = iris.target # Standardize the data scaler = StandardScaler() X = scaler.fit_transform(X) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42) # Convert numpy data to PyTorch tensors X_train = torch.tensor(X_train, dtype=torch.float32) X_test = torch.tensor(X_test, dtype=torch.float32) y_train = torch.tensor(y_train, dtype=torch.int64) y_test = torch.tensor(y_test, dtype=torch.int64) return X_train, X_test, y_train, y_test def evaluate_predictions(y_test, y_pred): return accuracy_score(y_test, y_pred) #model.py import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader, TensorDataset class IrisClassifier(nn.Module): def __init__(self): super(IrisClassifier, self).__init__() self.fc = nn.Sequential( nn.Linear(4, 16), nn.ReLU(), nn.Linear(16, 3) ) def forward(self, x): return self.fc(x) def train_model(self, X_train, y_train, epochs, lr, batch_size): criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(self.parameters(), lr=lr) # Create DataLoader for batches dataset = TensorDataset(X_train, y_train) dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True) for epoch in range(epochs): for batch_X, batch_y in dataloader: optimizer.zero_grad() outputs = self(batch_X) loss = criterion(outputs, batch_y) loss.backward() optimizer.step() def predict(self, X_test): with torch.no_grad(): outputs = self(X_test) _, predicted = outputs.max(1) return predicted.numpy() #main.py from utils import load_data, evaluate_predictions from model import IrisClassifier as Classifier def main(): # Model training and evaluation """ inputs = tokenizer(input_text, return_tensors="pt").cuda() outputs = model.generate(**inputs, max_new_tokens=140) print(tokenizer.decode(outputs[0])) ``` --- In the following scenario, the Deepseek-Coder 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. ### 4. Lincense This code repository is licensed under the MIT License. The use of DeepSeek Coder model and weights is subject to the Model License. DeepSeek Coder supports commercial use. See the [LICENSE-MODEL](https://github.com/deepseek-ai/deepseek-coder/blob/main/LICENSE-MODEL) for more details. ### 5. Contact If you have any questions, please raise an issue or contact us at [agi_code@deepseek.com](mailto:agi_code@deepseek.com).