👁 DeepSeek Coder

[🏠Homepage] | [🤖 Chat with DeepSeek Coder] | [Discord] | [Wechat(微信)]

---

1. Introduction of Deepseek Coder

Deepseek Coder is composed of a series of code language models, each trained from scratch on 2T tokens, with a composition of 87% code and 13% natural language in both English and Chinese. 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 from scratch on 2T tokens, including 87% code and 13% linguistic data in both English and Chinese languages.

• Highly Flexible & Scalable: Offered in model sizes of 1.3B, 5.7B, 6.7B, 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-33b-base is a 33B parameter model with Grouped-Query Attention trained on 2 trillion tokens.

• Home Page: DeepSeek
• Repository: deepseek-ai/deepseek-coder
• Chat With DeepSeek Coder: DeepSeek-Coder

3. How to Use

Here give some examples of how to use our model.

1）Code Completion

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-33b-base", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-33b-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))

2）Code Insertion

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-33b-base", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-33b-base", trust_remote_code=True).cuda()
input_text = """<｜fim▁begin｜>def quick_sort(arr):
 if len(arr) <= 1:
 return arr
 pivot = arr[0]
 left = []
 right = []
<｜fim▁hole｜>
 if arr[i] < pivot:
 left.append(arr[i])
 else:
 right.append(arr[i])
 return quick_sort(left) + [pivot] + quick_sort(right)<｜fim▁end｜>"""
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):])

3）Repository Level Code Completion

from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-33b-base", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-33b-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").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=140)
print(tokenizer.decode(outputs[0]))

4. License

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.

See the LICENSE-MODEL for more details.

5. Contact

If you have any questions, please raise an issue or contact us at agi_code@deepseek.com.

DeepSeek Coder series • 9 items • Updated Nov 27, 2025 • 79