Improve the README when using PEFT

[pacman100]

What does this PR do?

1. Adds peft library tag
2. Adds peft version
3. Adds quantization information if it was used

Code:

trainer.push_to_hub()
trainer.model.push_to_hub(trainer.repo.local_dir)

Sample Output:

model: https://huggingface.co/smangrul/results

---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: results
  results: []
library_name: peft
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# results

This model is a fine-tuned version of [tiiuae/falcon-7b](https://huggingface.co/tiiuae/falcon-7b) on an unknown dataset.

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure


The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: bfloat16
### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2560
- eval_batch_size: 40
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 10240
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- training_steps: 1875

### Framework versions

- PEFT 0.4.0.dev0
- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.3

[HuggingFaceDocBuilderDev]

The documentation is not available anymore as the PR was closed or merged.

[younesbelkada]

Super nice and clean 🔥 Thanks a lot @pacman100 !