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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: finetune_starcoder2_with_R_data |
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results: [] |
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datasets: |
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- bigcode/the-stack |
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language: |
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- en |
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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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# finetune_starcoder2_with_R_data |
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This model is a fine-tuned version of [bigcode/starcoder2-3b](https://huggingface.co/bigcode/starcoder2-3b), adapted and fine-tuned specifically for generating R programming code. |
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## Model description |
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This model is a specialized version of the bigcode/starcoder2-3b architecture fine-tuned on a subset of the Stack dataset, focusing solely on R programming language data. The fine-tuning process utilized the PEFT (Parameter Efficient Fine Tuning) method and included loading the model with 4-bit quantization using the LoRA library. It's tailored for generating R programming code, offering optimized performance for tasks within this domain. |
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## Intended uses & limitations |
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Tailored for R programming tasks, this model is optimized for generating code snippets, functions, or scripts in the R language. Its limitations may include its applicability solely within the domain of R programming and potential constraints related to the size and diversity of the training data. |
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## Training and evaluation data |
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The model was trained and evaluated on a subset of the bigcode/Stack dataset containing R programming language data. |
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## Training procedure |
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Fine-tuning was performed using the PEFT (Parameter Efficient Fine Tuning) method over 1000 epochs on the R dataset. Additionally, the model was loaded with 4-bit quantization using the LoRA library to optimize memory usage and inference speed, enhancing its efficiency for generating R code. |
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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: 2 |
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- eval_batch_size: 16 |
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- seed: 0 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 8 |
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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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### Framework versions |
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- PEFT 0.8.2 |
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- Transformers 4.40.0.dev0 |
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- Pytorch 2.1.2 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.2 |
