169 lines
6.7 KiB
Markdown
169 lines
6.7 KiB
Markdown
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---
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license: other
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license_name: deepseek-license
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license_link: LICENSE
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---
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<p align="center">
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<img width="1000px" alt="DeepSeek Coder" src="https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/pictures/logo.png?raw=true">
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</p>
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<p align="center"><a href="https://www.deepseek.com/">[🏠Homepage]</a> | <a href="https://coder.deepseek.com/">[🤖 Chat with DeepSeek Coder]</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 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.
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- **Massive Training Data**: Trained from scratch 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 1.3B, 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. Model Summary
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deepseek-coder-1.3b-base is a 1.3B parameter model with Multi-Head Attention trained on 1 trillion tokens.
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- **Home Page:** [DeepSeek](https://deepseek.com/)
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- **Repository:** [deepseek-ai/deepseek-coder](https://github.com/deepseek-ai/deepseek-coder)
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- **Chat With DeepSeek Coder:** [DeepSeek-Coder](https://coder.deepseek.com/)
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### 3. How to Use
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Here give 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-1.3b-base", trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-1.3b-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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#### 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-1.3b-base", trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-1.3b-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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#### 3）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-1.3b-base", trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-1.3b-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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### 4. 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-MODEL](https://github.com/deepseek-ai/deepseek-coder/blob/main/LICENSE-MODEL) for more details.
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### 5. 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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