Update README.md

README.md

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+# 🚀 Codelander
+
+---
+
+## 📖 Overview
+
+This specialized **CodeT5** model has been fine-tuned for **C++ code completion** tasks.  
+It excels at understanding **C++ syntax** and **common programming patterns** to provide intelligent code suggestions as you type.
+
+---
+
+## ✨ Key Features
+
+- 🔹 Context-aware completions for C++ functions, classes, and control structures  
+- 🔹 Handles complex C++ syntax including **templates, STL, and modern C++ features**  
+- 🔹 Trained on **competitive programming solutions** from high-quality Codeforces submissions  
+- 🔹 Low latency suitable for **real-time editor integration**  
+
+---
+
+## 📊 Model Performance
+
+| Metric              | Value   |
+|---------------------|---------|
+| Training Loss       | 1.2475  |
+| Validation Loss     | 1.0016  |
+| Training Epochs     | 3       |
+| Training Steps      | 14010   |
+| Samples per second  | 6.275   |
+
 ---
-license: apache-2.0
-language:
-- cpp
-metrics:
-- bleu
-library_name: transformers
-pipeline_tag: text-generation
-tags:
-- code-generation
-- code-completion
-- competitive-programming
+
+## ⚙️ Installation & Usage
+
+### 🔧 Direct Integration with HuggingFace Transformers
+
+```python
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+
+# Load model and tokenizer
+model = AutoModelForSeq2SeqLM.from_pretrained("outlander23/codelander")
+tokenizer = AutoTokenizer.from_pretrained("outlander23/codelander")
+
+# Generate completion
+def get_completion(code_prefix, max_new_tokens=100):
+    inputs = tokenizer(f"complete C++ code: {code_prefix}", return_tensors="pt")
+    outputs = model.generate(
+        inputs.input_ids,
+        max_new_tokens=max_new_tokens,
+        temperature=0.7,
+        top_p=0.9,
+        do_sample=True
+    )
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+```
+
 ---
 
+## 🏗️ Model Architecture
+
+- Base Model: **Salesforce/codet5-base**  
+- Parameters: **220M**  
+- Context Window: **512 tokens**  
+- Fine-tuning: **Seq2Seq training on C++ code snippets**  
+- Training Time: ~ **5 hours**  
+
+---
 
-# CodeLanderAI Model
+## 📂 Training Data
 
-This model is a fine-tuned version of the `CodeT5` model specifically designed for code completion in competitive programming. It was trained on a custom dataset of 12 million code samples derived from 2 million source code files.
+- Dataset: **open-r1/codeforces-submissions**  
+- Selection: **Accepted C++ solutions only**  
+- Size: **50,000+ code samples**  
+- Processing: **Prefix-suffix pairs with random splits**  
 
-## Intended Use
+---
 
-The model is intended for generating code completions based on the context provided by the user. It only supports cpp programming languages commonly used in competitive programming.
+## ⚠️ Limitations
 
-### Languages Supported
-- C++
+- ❌ May generate syntactically correct but semantically incorrect code  
+- ❌ Limited knowledge of **domain-specific libraries** not present in training data  
+- ❌ May occasionally produce **incomplete code fragments**  
 
+---
 
-### Metrics
+## 💻 Example Completions
 
-The model was evaluated using the following metrics:
+### ✅ Example 1: Factorial Function
 
-- **BLEU Score:** Measures the quality of generated code against reference code.
-- **CodeBLEU:** A metric tailored for code generation, considering syntax and structure.
-- **Accuracy:** How often the model provides the correct code completion.
-- **Perplexity:** Indicates how well the model predicts the next token in a sequence.
+**Input:**  
+```cpp
+int factorial(int n) {
+    if (n <= 1) {
+        return 1;
+    } else {
+```
+
+**Completion:**  
+```cpp
+        return n * factorial(n - 1);
+    }
+}
+```
+
+---
+
+### ✅ Example 2: Merge Sort
+
+**Input:**  
+```cpp
+vector<int> merge_sort(vector<int>& arr) {
+    if (arr.size() <= 1) return arr;
+
+    int mid = arr.size() / 2;
+    vector<int> left(arr.begin(), arr.begin() + mid);
+    vector<int> right(arr.begin() + mid, arr.end());
+
+    left = merge_sort(left);
+    right = merge_sort(right);
+```
+
+**Completion:**  
+```cpp
+    vector<int> result;
+    int i = 0, j = 0;
+
+    while (i < left.size() && j < right.size()) {
+        if (left[i] < right[j]) {
+            result.push_back(left[i++]);
+        } else {
+            result.push_back(right[j++]);
+        }
+    }
+
+    while (i < left.size()) result.push_back(left[i++]);
+    while (j < right.size()) result.push_back(right[j++]);
+
+    return result;
+}
+```
+
+---
+
+## 📈 Training Details
+
+- Training completed on: **2025-08-28 12:51:09 UTC**  
+- Training epochs: **3/3**  
+- Total steps: **14010**  
+- Training loss: **1.2475**  
+
+### 📊 Epoch Performance
+
+| Epoch | Training Loss | Validation Loss |
+|-------|---------------|-----------------|
+| 1     | 1.2638        | 1.1004          |
+| 2     | 1.1551        | 1.0250          |
+| 3     | 1.1081        | 1.0016          |
+
+---
+
+## 🖥️ Compatibility
+
+- ✅ Compatible with **Transformers 4.30.0+**  
+- ✅ Optimized for **Python 3.8+**  
+- ✅ Supports both **CPU and GPU inference**  
+
+---
 
-### Datasets
+## ❤️ Credits
 
-The model was fine-tuned on a custom dataset containing code samples from competitive programming platforms.
+Made with ❤️ by **outlander23**  
 
+> "Good code is its own best documentation." – *Steve McConnell*  
 
+---