README.md

---
library_name: peft
datasets:
- xfordanita/code-summary-java
base_model: codellama/CodeLlama-7b-hf
---

# Model Card for Model ID

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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### Model Sources [optional]

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## Uses

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### Direct Use

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### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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### Recommendations

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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.

## How to Get Started with the Model

Use the code below to get started with the model.

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## Training Details

### Training Data

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### Training Procedure

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#### Preprocessing [optional]

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#### Training Hyperparameters


Training  on Free Kaggle GPU 2*(15GB VRAM) with the following params:

```py
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"
)
```
#### Speeds, Sizes, Times [optional]

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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