---
license: apache-2.0
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: tiiuae/falcon-7b-instruct
model-index:
- name: Falcon-7b-Finetuned-MBPP-Dataset-base
  results: []
---

<!-- 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. -->

# Falcon-7b-Finetuned-MBPP-Dataset-base

This model is a fine-tuned version of [tiiuae/falcon-7b-instruct](https://huggingface.co/tiiuae/falcon-7b-instruct) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9306

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.8233        | 0.07  | 50   | 1.5671          |
| 1.673         | 0.15  | 100  | 1.5646          |
| 1.635         | 0.22  | 150  | 1.5569          |
| 1.4232        | 0.29  | 200  | 1.5369          |
| 1.4397        | 0.37  | 250  | 1.5073          |
| 1.5663        | 0.44  | 300  | 1.4721          |
| 1.4632        | 0.51  | 350  | 1.4342          |
| 1.6059        | 0.59  | 400  | 1.3978          |
| 1.6951        | 0.66  | 450  | 1.3606          |
| 1.7563        | 0.73  | 500  | 1.3241          |
| 0.939         | 0.81  | 550  | 1.2867          |
| 0.8452        | 0.88  | 600  | 1.2481          |
| 1.1147        | 0.95  | 650  | 1.2084          |
| 0.8543        | 1.03  | 700  | 1.1682          |
| 0.6985        | 1.1   | 750  | 1.1356          |
| 1.0973        | 1.17  | 800  | 1.1100          |
| 2.0793        | 1.25  | 850  | 1.0892          |
| 0.9806        | 1.32  | 900  | 1.0713          |
| 0.8114        | 1.4   | 950  | 1.0555          |
| 1.4202        | 1.47  | 1000 | 1.0425          |
| 0.7755        | 1.54  | 1050 | 1.0314          |
| 0.8624        | 1.62  | 1100 | 1.0223          |
| 1.6017        | 1.69  | 1150 | 1.0143          |
| 1.069         | 1.76  | 1200 | 1.0071          |
| 1.2192        | 1.84  | 1250 | 1.0007          |
| 0.8816        | 1.91  | 1300 | 0.9944          |
| 0.9615        | 1.98  | 1350 | 0.9887          |
| 1.2626        | 2.06  | 1400 | 0.9833          |
| 1.0128        | 2.13  | 1450 | 0.9787          |
| 0.7951        | 2.2   | 1500 | 0.9741          |
| 1.0879        | 2.28  | 1550 | 0.9701          |
| 1.0546        | 2.35  | 1600 | 0.9661          |
| 0.9218        | 2.42  | 1650 | 0.9625          |
| 1.1159        | 2.5   | 1700 | 0.9591          |
| 0.6223        | 2.57  | 1750 | 0.9561          |
| 0.7334        | 2.64  | 1800 | 0.9536          |
| 0.9296        | 2.72  | 1850 | 0.9512          |
| 1.0653        | 2.79  | 1900 | 0.9489          |
| 0.8812        | 2.86  | 1950 | 0.9469          |
| 0.7767        | 2.94  | 2000 | 0.9452          |
| 0.9707        | 3.01  | 2050 | 0.9435          |
| 1.1393        | 3.08  | 2100 | 0.9420          |
| 0.8604        | 3.16  | 2150 | 0.9407          |
| 0.7592        | 3.23  | 2200 | 0.9396          |
| 0.8046        | 3.3   | 2250 | 0.9385          |
| 1.5882        | 3.38  | 2300 | 0.9375          |
| 1.0068        | 3.45  | 2350 | 0.9366          |
| 1.205         | 3.52  | 2400 | 0.9357          |
| 0.689         | 3.6   | 2450 | 0.9350          |
| 0.8573        | 3.67  | 2500 | 0.9344          |
| 1.072         | 3.74  | 2550 | 0.9338          |
| 0.9188        | 3.82  | 2600 | 0.9332          |
| 1.3385        | 3.89  | 2650 | 0.9327          |
| 0.9067        | 3.96  | 2700 | 0.9324          |
| 0.9993        | 4.04  | 2750 | 0.9321          |
| 0.8222        | 4.11  | 2800 | 0.9317          |
| 0.8129        | 4.19  | 2850 | 0.9315          |
| 0.7861        | 4.26  | 2900 | 0.9313          |
| 1.3126        | 4.33  | 2950 | 0.9311          |
| 0.9465        | 4.41  | 3000 | 0.9310          |
| 0.9444        | 4.48  | 3050 | 0.9309          |
| 0.5677        | 4.55  | 3100 | 0.9308          |
| 0.7046        | 4.63  | 3150 | 0.9307          |
| 1.5036        | 4.7   | 3200 | 0.9307          |
| 1.0087        | 4.77  | 3250 | 0.9307          |
| 0.6705        | 4.85  | 3300 | 0.9306          |
| 1.0425        | 4.92  | 3350 | 0.9306          |
| 0.3666        | 4.99  | 3400 | 0.9306          |


### Framework versions

- PEFT 0.10.1.dev0
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2