Prasanna Dhungana
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README.md
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# finetune_starcoder2_with_R_data
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This model is a
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## Model description
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## Intended uses & limitations
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## Training and evaluation data
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## Training procedure
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### Training hyperparameters
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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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### Framework versions
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# finetune_starcoder2_with_R_data
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This model is a variant of the bigcode/starcoder2-3b architecture, adapted and fine-tuned specifically for generating R programming code.
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## Model description
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Model is
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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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- 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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