---
base_model: EleutherAI/pythia-31m
tags:
- generated_from_trainer
metrics:
- accuracy
inference:
  parameters:
    max_new_tokens: 64
    do_sample: true
    repetition_penalty: 1.1
    no_repeat_ngram_size: 5
    guidance_scale: 1.01
    eta_cutoff: 0.001
widget:
  - text: My name is El Microondas the Wise and
    example_title: El Microondas
  - text: A meme is
    example_title: meme
  - text: >-
      Barack Obama nominated Hilary Clinton as his secretary of state on Monday.
      He chose her because she had
    example_title: Coreference resolution
  - text: >-
      On a shelf, there are five books: a gray book, a red book, a purple book,
      a blue book, and a black book
    example_title: Logic puzzles
  - text: >-
      The two men running to become New York City's next mayor will face off in
      their first debate Wednesday night
    example_title: Reading comprehension
license: apache-2.0
datasets:
- pszemraj/simple_wikipedia_LM
pipeline_tag: text-generation
---

<!-- 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. -->

# pythia-31m-simplewiki-scratch-bf16

Trained from random initialized config based on [EleutherAI/pythia-31m](https://huggingface.co/EleutherAI/pythia-31m), 3 epochs bf16
It achieves the following results on the evaluation set:
- Loss: 4.1763
- Accuracy: 0.3676

## Model description

tuned with bf16 (previous was fp32)

## Intended uses & limitations

More information needed

## Training and evaluation data

```
***** eval metrics *****                                              
  epoch                   =       2.99                   
  eval_accuracy           =     0.3723                                  eval_loss               =     4.1155                                
  eval_runtime            = 0:00:14.44                                
  eval_samples            =        500                                  eval_samples_per_second =     34.602                                  eval_steps_per_second   =     17.301                              
  perplexity              =    61.2811
```

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 2
- eval_batch_size: 2
- seed: 80085
- gradient_accumulation_steps: 64
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.99) and epsilon=1e-07
- lr_scheduler_type: inverse_sqrt
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 3.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 5.8617        | 0.45  | 100  | 5.5276          | 0.2451   |
| 5.2782        | 0.9   | 200  | 4.9596          | 0.2965   |
| 4.9996        | 1.35  | 300  | 4.6412          | 0.3310   |
| 4.6292        | 1.8   | 400  | 4.4344          | 0.3485   |
| 4.5339        | 2.25  | 500  | 4.2875          | 0.3600   |
| 4.5214        | 2.7   | 600  | 4.1763          | 0.3676   |


### Framework versions

- Transformers 4.33.1
- Pytorch 2.2.0.dev20230907+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3