---
tags:
- generated_from_trainer
datasets:
- /pfs/lustrep4/scratch/project_462000259/shared_datasets/modified_200/modified_200/
metrics:
- accuracy
model-index:
- name: layer_0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31
  results:
  - task:
      name: Causal Language Modeling
      type: text-generation
    dataset:
      name: /pfs/lustrep4/scratch/project_462000259/shared_datasets/modified_200/modified_200/
      type: /pfs/lustrep4/scratch/project_462000259/shared_datasets/modified_200/modified_200/
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.05919863214460186
---

<!-- 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. -->

# layer_0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31

This model is a fine-tuned version of [/pfs/lustrep4/scratch/project_462000259/shared_models/pythia-2.8b-deduped-base/pythia-2.8b-deduped](https://huggingface.co//pfs/lustrep4/scratch/project_462000259/shared_models/pythia-2.8b-deduped-base/pythia-2.8b-deduped) on the /pfs/lustrep4/scratch/project_462000259/shared_datasets/modified_200/modified_200/ dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9966
- Accuracy: 0.0592

## 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: 5e-05
- train_batch_size: 1
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 16
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.99) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 14484

### Training results



### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.1+rocm5.4.2
- Datasets 2.11.0
- Tokenizers 0.13.3