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--- |
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license: bigcode-openrail-m |
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library_name: peft |
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tags: |
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- trl |
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- sft |
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- generated_from_trainer |
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base_model: bigcode/starcoder2-3b |
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model-index: |
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- name: finetunedPHP_starcoder2 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# finetunedPHP_starcoder2 |
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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). |
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## Model description |
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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. |
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## Intended uses & limitations |
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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. |
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## Training and evaluation data |
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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. |
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## Training procedure |
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**1. Data and Model Preparation:** |
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- Load the PHP dataset from my repository `bigcode/the-stack-smol`. |
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- Extract the relevant PHP data `data/php` samples for training. |
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- Utilize the `starcoder2-3b` model pre-trained on a diverse range of programming languages, including PHP, from the Hugging Face Hub. |
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- Ensure the model is configured with '4-bit' quantization for efficient computation. |
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**2. Data Processing:** |
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- Tokenize the PHP code snippets using the model's tokenizer. |
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- Clean the code by removing comments and normalizing indentation. |
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- Prepare input examples suitable for the model, considering its architecture and objectives. |
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**3. Training Configuration:** |
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- Initialize a Trainer object for fine-tuning, leveraging the Transformers library. |
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- Define training parameters, including: |
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- Learning rate, optimizer, and scheduler settings. |
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- Gradient accumulation steps to balance memory usage. |
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- Loss function, typically cross-entropy for language modeling. |
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- Metrics for evaluating model performance. |
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- Specify GPU utilization for accelerated training. |
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- Handle potential distributed training with multiple processes. |
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**4. Model Training:** |
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- Commence training for a specified number of steps. |
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- Iterate through batches of preprocessed PHP code examples. |
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- Feed examples into the model and compute predictions. |
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- Calculate loss based on predicted and actual outcomes. |
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- Update model weights by backpropagating gradients through the network. |
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**5. Evaluation (Optional):** |
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- Periodically assess the model's performance on a validation set. |
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- Measure key metrics such as code completion accuracy or perplexity. |
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- Monitor training progress to fine-tune hyperparameters if necessary. |
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- Use wandb metric monitoring for live monitoring. |
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**6. Save the Fine-tuned Model:** |
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- Store the optimized model weights and configuration in the designated `output_dir`. |
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**7. Model Sharing (Optional):** |
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- Optionally, create a model card documenting the fine-tuning process and model specifications. |
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- Share the finetunedPHP_starcoder2 model on the Hugging Face Hub for broader accessibility and collaboration. |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0002 |
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- train_batch_size: 1 |
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- eval_batch_size: 8 |
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- seed: 0 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 4 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 100 |
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- training_steps: 1000 |
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- mixed_precision_training: Native AMP |
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### Training results |
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Training results and performance metrics are present in the repo. |
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### Framework versions |
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- PEFT 0.8.2 |
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- Transformers 4.40.0.dev0 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
