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This model is a fine-tuned version of codellama/CodeLlama-7b-hf on the QLoRA by using the method PEFT with library..
Model Details
Model Description
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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Training Details
Training Data
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Training Procedure
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Training Hyperparameters
Training on Free Kaggle GPU 2*(15GB VRAM) with the following params:
training_arguments = TrainingArguments(
output_dir='./results',
num_train_epochs=8,
per_device_train_batch_size=4,
gradient_accumulation_steps=2,
optim="paged_adamw_32bit",
save_steps=0,
logging_steps=10,
learning_rate=2e-4,
weight_decay=0.1, # Utilisation d'une valeur plus élevée pour la régularisation L2
fp16=True,
max_grad_norm=1.0, # Réduire la taille maximale des gradients pour éviter les explosions de gradients
max_steps=-1,
warmup_ratio=0.1, # Augmentation du ratio de warmup
group_by_length=True,
lr_scheduler_type="constant", # Utilisation d'un taux d'apprentissage constant
report_to="tensorboard"
)
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Evaluation
Testing Data, Factors & Metrics
Testing Data
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Factors
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Results
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Summary
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Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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Technical Specifications [optional]
Model Architecture and Objective
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Base model
codellama/CodeLlama-7b-hf