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---
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
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_pszemraj__pythia-31m-simplewiki-scratch-bf16)
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | 24.63 |
| ARC (25-shot) | 22.78 |
| HellaSwag (10-shot) | 25.61 |
| MMLU (5-shot) | 23.12 |
| TruthfulQA (0-shot) | 49.65 |
| Winogrande (5-shot) | 50.51 |
| GSM8K (5-shot) | 0.0 |
| DROP (3-shot) | 0.72 |
|