mirror of
https://github.com/deepseek-ai/DeepSeek-Coder.git
synced 2025-04-20 02:29:09 -04:00
163 lines
7.1 KiB
Markdown
163 lines
7.1 KiB
Markdown
|
|
### 1. Introduction of Deepseek Coder
|
|
|
|
Deepseek Coder comprises a series of advanced 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 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-X**](), [**MBPP**](), [**DS-1000**](), and [**APPS**]() datasets.
|
|
|
|
- **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. Procedure of Data Creation and Model Training
|
|
|
|
#### Data Creation
|
|
|
|
- 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.
|
|
- Step 2: Parsing the dependencies of files within the same repository to rearrange the file positions based on their dependencies.
|
|
- Step 3: Concatenating dependent files to form a single example and employ repo-level minhash for deduplication.
|
|
- Step 4: Further filtering out low-quality code, such as codes with syntax errors or poor readability.
|
|
|
|
|
|
|
|
#### Model Training
|
|
|
|
- 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. This process involves 1.8T tokens and uses a 4K window size.
|
|
- Step 2: Further Pre-training using an extended 16K window size on an additional 200B tokens, resulting in foundational models.
|
|
- Step 3: Instruction Fine-tuning on 300M tokens of instruction data, resulting in instruction-tuned models.
|
|
|
|
|
|
|
|
|
|
|
|
### 3. Download and Setup
|
|
Deepseek Coder is initially implemented in Pytorch and trained on A100 AI Processors. We provide a torch-compatible version based on hai-llm to facilitate usage on GPU platforms. We also uploaded the checkpoint of models to the 🤗 [hugginface](https://huggingface.co/deepseek-ai/deepseek-coder-7b).
|
|
#### Setup
|
|
Python 3.8+ / CUDA 11+ / PyTorch 2.0+ / transformers 3.34+ are required.
|
|
### 4. Inference and Evaluation
|
|
Here give some examples of how to use our model.
|
|
#### Code Completion
|
|
```python
|
|
from transformers import AutoTokenizer, AutoModelForCausalLM
|
|
tokenizer = AutoTokenizer.from_pretrained("deepseek/deepseek-coder-7b")
|
|
device = 0 if torch.cuda.is_available() else -1
|
|
model = AutoModelForCausalLM.from_pretrained("deepseek/deepseek-coder-7b").to(device)
|
|
inputs = tokenizer("def hello_world():", return_tensors="pt").to(device)
|
|
outputs = model.generate(**inputs, max_length=128)
|
|
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
|
|
```
|
|
|
|
#### Code Insertion
|
|
```python
|
|
from transformers import AutoTokenizer, AutoModelForCausalLM
|
|
tokenizer = AutoTokenizer.from_pretrained("deepseek/deepseek-coder-7b")
|
|
device = 0 if torch.cuda.is_available() else -1
|
|
model = AutoModelForCausalLM.from_pretrained("deepseek/deepseek-coder-7b").to(device)
|
|
input_text = "<fim_prefix>def print_hello_world():\n <fim_suffix>\n print('Hello world!')<fim_middle>"
|
|
inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
|
|
outputs = model.generate(**inputs, max_length=128)
|
|
print(tokenizer.decode(outputs[0]))
|
|
```
|
|
#### Repository Level Code Completion
|
|
```python
|
|
from transformers import AutoTokenizer, AutoModelForCausalLM
|
|
tokenizer = AutoTokenizer.from_pretrained("deepseek/deepseek-coder-7b")
|
|
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
|
|
|
|
def pipeline():
|
|
# Model training and evaluation
|
|
"""
|
|
inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
|
|
outputs = model.generate(**inputs, max_length=128)
|
|
print(tokenizer.decode(outputs[0]))
|
|
```
|
|
#### Chat Model Inference
|
|
```python
|
|
from transformers import AutoTokenizer, AutoModelForCausalLM
|
|
tokenizer = AutoTokenizer.from_pretrained("deepseek/deepseek-coder-7b")
|
|
prompt = "write a quick sort algorithm in python."
|
|
prompt = f"""Below is an instruction that describes a task, paired with an input that provides further context.\nWrite a response that appropriately completes the request.\n\n### Instruction:\nWrite a program to perform the given task.\n\nInput:\n{prompt}\n\n### Response:\n"""
|
|
inputs = tokenizer.encode(prompt, return_tensors="pt").to(device)
|
|
outputs = model.generate(**inputs, max_length=128)
|
|
print(tokenizer.decode(outputs[0]))
|
|
```
|
|
### 5. Lincense
|
|
|
|
### 6. Citation
|
|
|
|
|
|
|