---
license: llama3
library_name: peft
tags:
- llama-factory
- generated_from_trainer
base_model: meta-llama/Meta-Llama-3-70B-Instruct
model-index:
- name: llama3-70B-lora-pretrain_v2
  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. -->

# llama3-70B-lora-pretrain_v2

This model is a fine-tuned version of [meta-llama/Meta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9383

## 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: 0.0001
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- total_eval_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 500
- num_epochs: 3.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.6995        | 0.0939 | 100  | 2.6305          |
| 2.4199        | 0.1877 | 200  | 2.3979          |
| 2.2722        | 0.2816 | 300  | 2.2180          |
| 2.0762        | 0.3754 | 400  | 2.1251          |
| 1.9652        | 0.4693 | 500  | 2.0858          |
| 2.1893        | 0.5631 | 600  | 2.0629          |
| 2.0153        | 0.6570 | 700  | 2.0473          |
| 1.9911        | 0.7508 | 800  | 2.0318          |
| 2.1041        | 0.8447 | 900  | 2.0198          |
| 2.0488        | 0.9385 | 1000 | 2.0117          |
| 1.897         | 1.0324 | 1100 | 2.0018          |
| 2.0298        | 1.1262 | 1200 | 1.9952          |
| 2.0989        | 1.2201 | 1300 | 1.9890          |
| 1.8695        | 1.3139 | 1400 | 1.9838          |
| 2.1573        | 1.4078 | 1500 | 1.9764          |
| 2.0183        | 1.5016 | 1600 | 1.9713          |
| 1.9229        | 1.5955 | 1700 | 1.9672          |
| 1.9732        | 1.6893 | 1800 | 1.9617          |
| 1.6835        | 1.7832 | 1900 | 1.9574          |
| 1.9874        | 1.8771 | 2000 | 1.9539          |
| 1.7607        | 1.9709 | 2100 | 1.9512          |
| 1.9459        | 2.0648 | 2200 | 1.9480          |
| 1.7611        | 2.1586 | 2300 | 1.9463          |
| 1.8491        | 2.2525 | 2400 | 1.9441          |
| 1.9121        | 2.3463 | 2500 | 1.9427          |
| 1.8849        | 2.4402 | 2600 | 1.9413          |
| 2.0679        | 2.5340 | 2700 | 1.9400          |
| 1.9908        | 2.6279 | 2800 | 1.9394          |
| 1.9557        | 2.7217 | 2900 | 1.9388          |
| 1.9627        | 2.8156 | 3000 | 1.9384          |
| 1.8339        | 2.9094 | 3100 | 1.9383          |


### Framework versions

- PEFT 0.10.0
- Transformers 4.40.0
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.19.1