Update README.md

README.md

@@ -36,6 +36,37 @@ Ruby Code Generator is a versatile tool crafted to streamline the interaction be
 
 ## Training procedure
 
+**1. Load Dataset and Model:**
+    - Load the bigcode/the-stack-smol dataset using the Hugging Face Datasets library.
+    - Filter for the specified subset (data/ruby) and split (train).
+    - Load the bigcode/starcoder2-3b model from the Hugging Face Hub with '4-bit' quantization.
+**2. Data Preprocessing:**
+    - Tokenize the code text using the appropriate tokenizer for the chosen model.
+    - Apply necessary cleaning or normalization (e.g., removing comments, handling indentation).
+    - Create input examples suitable for the model's architecture (e.g., with masked language modeling objectives).
+**3. Configure Training:**
+    - Initialize a Trainer object (likely from a library like Transformers).
+    - Set training arguments based on the provided args:
+      - Learning rate, optimizer, scheduler
+      - Gradient accumulation steps
+      - Weight decay
+      - Loss function (likely cross-entropy)
+      - Evaluation metrics (e.g., accuracy, perplexity)
+      - Device placement (GPU/TPU)
+      - Number of processes for potential distributed training
+
+**4. Train the Model:**
+
+  - Start the training loop for the specified max_steps.
+  - Iterate through batches of preprocessed code examples.
+  - Forward pass through the model to generate predictions.
+  - Calculate loss based on ground truth and predictions.
+  - Backpropagate gradients to update model parameters.
+    
+**6. Save the Fine-tuned Model:**
+
+  - Save the model's weights and configuration to the output_dir.
+
 ### Training hyperparameters
 
 The following hyperparameters were used during training: