Update README.md

README.md

@@ -18,11 +18,12 @@ should probably proofread and complete it, then remove this comment. -->
 
 # finetune_starcoder2
 
-This model is a fine-tuned version of [bigcode/starcoder2-3b](https://huggingface.co/bigcode/starcoder2-3b) on an unknown dataset.
+This model is a fine-tuned version of [bigcode/starcoder2-3b](https://huggingface.co/bigcode/starcoder2-3b) on [bigcode/the-stack-smol](https://huggingface.co/datasets/bigcode/the-stack-smol).
 
 ## Model description
 
-More information needed
+This fine-tuned model builds upon the `bigcode/starcoder2-3b` base model, further specializing it for code completion tasks using the rich `bigcode/the-stack-smol` dataset on SQL data. This dataset focuses on code snippets and solutions, allowing the model to suggest relevant completions and potentially even generate code based on your prompts.
+
 
 ## Intended uses & limitations
 
@@ -34,6 +35,52 @@ More information needed
 
 ## 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/sql`) and split (`train`).
+- Load the `bigcode/starcoder2-3b` model from the Hugging Face Hub with '4-bit' quantization.
+
+**2. Preprocess Data:**
+
+- 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.
+
+**5. Evaluation (Optional):**
+
+- Periodically evaluate model performance on a validation or test set.
+- Calculate relevant metrics (accuracy, perplexity, code completion accuracy).
+- Monitor training progress and adjust hyperparameters as needed.
+
+**6. Save the Fine-tuned Model:**
+
+- Save the model's weights and configuration to the `output_dir`.
+
+**7. Push to Hugging Face Hub (Optional):**
+
+- If `push_to_hub` is True, create a model card and push the model to Hugging Face Hub for sharing and use.
+
 ### Training hyperparameters
 
 The following hyperparameters were used during training: