Update README.md

README.md

@@ -3,164 +3,18 @@ license: other
 license_name: deepseek-license
 license_link: LICENSE
 ---
+# deepseek-coder-6.7B-chat
+It was created by starting with the deepseek-coder-6.7B and training it on the open assistant dataset. We have attached the wandb report in pdf form to view the training run at a glance.
 
-<p align="center">
-<img width="1000px" alt="DeepSeek Coder" src="https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/pictures/logo.png?raw=true">
-</p>
-<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>
-<hr>
+# Reson
+This model was fine tned to allow it to follow direction and is a steeping stone to further training, but still would be good for asking qestions about code.
 
+# Templete
+Us the following templete when interacting with the fine tuned model.
 
-### 1. Introduction of Deepseek Coder
+# Getting Started
+A quick start quide.
 
-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-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. 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](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).
+# Referrals
+Run Pod - This is who I use to train th emodels on huggingface. If you use it we both get free crdits.
+Paypal - If you want to leave a tip, it is appecaheted.