Refactor of models and trainers with base class for common methods

[PierpaoloSorbellini]

• Refactor models and trainers to avoid code replication.
• Added logs with loguru package.
• Fix logs with MultiGPU trainers.
• Added support for LoRA with PEFT library.
• Added support for load_8bit option with HF models.
• Added self-instruct dataset of HF.
• Added CerebrasGPT and Decapoda LLaMA models from HF.
• Added mixed-precision training to reduce GPU memory requirements.
• Fixed RLHF KL divergence equation.
• Added support to keep only the last n checkpoints for all training.
• Added generation of negative examples when creating the reward dataset to improve the quality of the reward model.
• Improved stability of MultiGPU training with both Accelerate form HF and DeepSpeed.

[diegofiori]

@PierpaoloSorbellini please add a description of what this PR is adding in terms of features and which bugs it is fixing.