---
base_model: meta-llama/Llama-2-7b-hf
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: Llama-2-7b-hf-finetuned-mrpc-v0.4
  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. -->

# Llama-2-7b-hf-finetuned-mrpc-v0.4

This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4717
- Accuracy: 0.8676
- F1: 0.9046

## 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: True
- load_in_4bit: False
- 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: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1.6000000000000003e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Accuracy | F1     | Validation Loss |
|:-------------:|:-----:|:----:|:--------:|:------:|:---------------:|
| No log        | 1.0   | 230  | 0.6446   | 0.7695 | 0.6542          |
| No log        | 2.0   | 460  | 0.6912   | 0.7968 | 0.5938          |
| 0.6489        | 3.0   | 690  | 0.7230   | 0.8151 | 0.5694          |
| 0.6489        | 4.0   | 920  | 0.7230   | 0.8138 | 0.5503          |
| 0.5299        | 5.0   | 1150 | 0.7402   | 0.8251 | 0.5492          |
| 0.5299        | 6.0   | 1380 | 0.7794   | 0.8432 | 0.4880          |
| 0.4687        | 7.0   | 1610 | 0.8064   | 0.8663 | 0.4559          |
| 0.4687        | 8.0   | 1840 | 0.8186   | 0.875  | 0.4298          |
| 0.374         | 9.0   | 2070 | 0.8284   | 0.8818 | 0.4210          |
| 0.374         | 10.0  | 2300 | 0.8456   | 0.8916 | 0.3953          |
| 0.3096        | 11.0  | 2530 | 0.8431   | 0.8897 | 0.4074          |
| 0.3096        | 12.0  | 2760 | 0.8407   | 0.8862 | 0.4030          |
| 0.3096        | 13.0  | 2990 | 0.8456   | 0.8904 | 0.3982          |
| 0.2799        | 14.0  | 3220 | 0.8456   | 0.8881 | 0.3873          |
| 0.2799        | 15.0  | 3450 | 0.8529   | 0.8940 | 0.3939          |
| 0.2511        | 16.0  | 3680 | 0.8431   | 0.8877 | 0.4018          |
| 0.2511        | 17.0  | 3910 | 0.8529   | 0.8947 | 0.3969          |
| 0.2371        | 18.0  | 4140 | 0.8456   | 0.8912 | 0.3963          |
| 0.2371        | 19.0  | 4370 | 0.8578   | 0.8964 | 0.3865          |
| 0.2211        | 20.0  | 4600 | 0.8505   | 0.8928 | 0.4165          |
| 0.2211        | 21.0  | 4830 | 0.4070   | 0.8456 | 0.8901          |
| 0.2136        | 22.0  | 5060 | 0.4090   | 0.8578 | 0.8972          |
| 0.2136        | 23.0  | 5290 | 0.4328   | 0.8578 | 0.8961          |
| 0.1774        | 24.0  | 5520 | 0.4602   | 0.8382 | 0.8791          |
| 0.1774        | 25.0  | 5750 | 0.4551   | 0.8627 | 0.9018          |
| 0.1774        | 26.0  | 5980 | 0.4677   | 0.8505 | 0.8920          |
| 0.1521        | 27.0  | 6210 | 0.4854   | 0.8578 | 0.8953          |
| 0.1521        | 28.0  | 6440 | 0.5064   | 0.8505 | 0.8932          |
| 0.134         | 29.0  | 6670 | 0.4971   | 0.8603 | 0.8988          |
| 0.134         | 30.0  | 6900 | 0.4717   | 0.8676 | 0.9046          |


### Framework versions

- PEFT 0.4.0
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3