Create README.md

README.md

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+---
+license: other
+license_name: deepseek-license
+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 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-6.7b-base is a 6.7B parameter model with Multi-Head 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-6.7b-base", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-6.7b-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
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-6.7b-base", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-6.7b-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
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-6.7b-base", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-6.7b-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]))
+```
+
+
+
+### 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 [[email protected]](mailto:[email protected]).