---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: togethercomputer/evo-1-8k-base
model-index:
- name: lora_evo_ta_all_layers_18_attention_layers
  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. -->

# lora_evo_ta_all_layers_18_attention_layers

This model is a fine-tuned version of [togethercomputer/evo-1-8k-base](https://huggingface.co/togethercomputer/evo-1-8k-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8474

## Model description

Trained on single ID token "5K dataset" filtered to 4k sequences (20% for test data)

lora_alpha = 64 <--------------

lora_dropout = 0.1

lora_r = 64 <---------

epochs = 3

learning rate = 3e-4

warmup_steps=500

gradient_accumulation_steps = 1

train_batch = 1

eval_batch = 1

ONLY ATTENTION LAYER <---------------------

## 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: 0.0003
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_steps: 500
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.0886        | 0.375 | 1200 | 3.0465          |
| 3.0274        | 0.75  | 2400 | 2.9992          |
| 2.9835        | 1.125 | 3600 | 2.9622          |
| 2.9334        | 1.5   | 4800 | 2.9397          |
| 2.8989        | 1.875 | 6000 | 2.9026          |
| 2.8609        | 2.25  | 7200 | 2.8744          |
| 2.8413        | 2.625 | 8400 | 2.8584          |
| 2.8341        | 3.0   | 9600 | 2.8474          |


### Framework versions

- PEFT 0.11.1
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1