Update README.md

README.md

@@ -16,22 +16,70 @@ should probably proofread and complete it, then remove this comment. -->
 
 # finetunedPHP_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
+The `finetunedPHP_starcoder2` model is based on the `starcoder2-3b` architecture, fine-tuned specifically on PHP code from the-stack-smol dataset. It is intended for code generation tasks related to PHP programming.
 
 ## Intended uses & limitations
 
-More information needed
+The `finetunedPHP_starcoder2` model is suitable for generating PHP code snippets for various purposes, including code completion, syntax suggestions, and code generation tasks. However, it may have limitations in generating complex or domain-specific code, and users should verify the generated code for correctness and security.
 
 ## Training and evaluation data
 
-More information needed
+The model was trained on a dataset consisting of PHP code samples collected from the-stack-smol dataset. The training data included code snippets from PHP repositories, forums, and online tutorials.
 
 ## Training procedure
 
+**1. Data and Model Preparation:**
+
+- Load the PHP dataset from my repository `bigcode/the-stack-smol`.
+- Extract the relevant PHP data `data/php` samples for training.
+- Utilize the `starcoder2-3b` model pre-trained on a diverse range of programming languages, including PHP, from the Hugging Face Hub.
+- Ensure the model is configured with '4-bit' quantization for efficient computation.
+
+**2. Data Processing:**
+
+- Tokenize the PHP code snippets using the model's tokenizer.
+- Clean the code by removing comments and normalizing indentation.
+- Prepare input examples suitable for the model, considering its architecture and objectives.
+
+**3. Training Configuration:**
+
+- Initialize a Trainer object for fine-tuning, leveraging the Transformers library.
+- Define training parameters, including:
+    - Learning rate, optimizer, and scheduler settings.
+    - Gradient accumulation steps to balance memory usage.
+    - Loss function, typically cross-entropy for language modeling.
+    - Metrics for evaluating model performance.
+    - Specify GPU utilization for accelerated training.
+    - Handle potential distributed training with multiple processes.
+
+**4. Model Training:**
+
+- Commence training for a specified number of steps.
+- Iterate through batches of preprocessed PHP code examples.
+- Feed examples into the model and compute predictions.
+- Calculate loss based on predicted and actual outcomes.
+- Update model weights by backpropagating gradients through the network.
+
+**5. Evaluation (Optional):**
+
+- Periodically assess the model's performance on a validation set.
+- Measure key metrics such as code completion accuracy or perplexity.
+- Monitor training progress to fine-tune hyperparameters if necessary.
+- Use wandb metric monitoring for live monitoring.
+
+**6. Save the Fine-tuned Model:**
+
+- Store the optimized model weights and configuration in the designated `output_dir`.
+
+**7. Model Sharing (Optional):**
+
+- Optionally, create a model card documenting the fine-tuning process and model specifications.
+- Share the finetunedPHP_starcoder2 model on the Hugging Face Hub for broader accessibility and collaboration.
+
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
@@ -49,7 +97,7 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-
+Training results and performance metrics are present in the repo.
 
 ### Framework versions