---
license: mit
base_model: gokulsrinivasagan/gpt_96
tags:
- generated_from_trainer
datasets:
- gokuls/wiki_book_corpus_complete_raw_dataset
metrics:
- accuracy
model-index:
- name: gpt_108
  results:
  - task:
      name: Causal Language Modeling
      type: text-generation
    dataset:
      name: gokuls/wiki_book_corpus_complete_raw_dataset
      type: gokuls/wiki_book_corpus_complete_raw_dataset
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.3821764665697512
---

<!-- 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. -->

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/gokulsrinivasagan/huggingface/runs/damp0hrm)
# gpt_108

This model is a fine-tuned version of [gokulsrinivasagan/gpt_96](https://huggingface.co/gokulsrinivasagan/gpt_96) on the gokuls/wiki_book_corpus_complete_raw_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1085
- Accuracy: 0.3822

## 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-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100

### Training results



### Framework versions

- Transformers 4.42.0.dev0
- Pytorch 2.2.2+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1