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---
license: bigcode-openrail-m
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: bigcode/starcoder2-3b
model-index:
- name: finetune_starcoder2
results: []
datasets:
- bigcode/the-stack-smol
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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 [bigcode/the-stack-smol](https://huggingface.co/datasets/bigcode/the-stack-smol).
## Model description
This fine-tuned model builds upon the `bigcode/starcoder2-3b` base model, further specializing it for code completion tasks using the `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
More information needed
## Training and evaluation data
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:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 0
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.8.2
- Transformers 4.40.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 |