metadata
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
datasets:
- pszemraj/simple_wikipedia_LM
pipeline_tag: text-generation
license: apache-2.0
pythia-31m-simplewiki-2048
This was initialized from random weights based on the config of EleutherAI/pythia-31m and trained on pszemraj/simple_wikipedia_LM for 3 epochs.
It achieves the following results on the evaluation set:
- Loss: 3.6874
- Accuracy: 0.4105
Model description
More information needed
Intended uses & limitations
This is a baseline for comparison to other models.
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 1
- eval_batch_size: 1
- seed: 80085
- gradient_accumulation_steps: 64
- total_train_batch_size: 64
- 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 |
|---|---|---|---|---|
| 6.0657 | 0.22 | 100 | 5.6210 | 0.2414 |
| 5.2447 | 0.45 | 200 | 4.9316 | 0.3054 |
| 4.8397 | 0.67 | 300 | 4.6011 | 0.3343 |
| 4.7933 | 0.9 | 400 | 4.3878 | 0.3530 |
| 4.274 | 1.12 | 500 | 4.2352 | 0.3646 |
| 4.4867 | 1.35 | 600 | 4.1224 | 0.3723 |
| 4.3434 | 1.57 | 700 | 4.0282 | 0.3791 |
| 4.1857 | 1.8 | 800 | 3.9552 | 0.3841 |
| 4.229 | 2.02 | 900 | 3.8890 | 0.3909 |
| 3.9189 | 2.25 | 1000 | 3.8301 | 0.3967 |
| 4.084 | 2.47 | 1100 | 3.7782 | 0.4023 |
| 3.8965 | 2.7 | 1200 | 3.7302 | 0.4069 |
| 3.915 | 2.92 | 1300 | 3.6874 | 0.4105 |
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
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 24.35 |
| ARC (25-shot) | 22.18 |
| HellaSwag (10-shot) | 25.55 |
| MMLU (5-shot) | 23.12 |
| TruthfulQA (0-shot) | 49.37 |
| Winogrande (5-shot) | 49.41 |
| GSM8K (5-shot) | 0.0 |
| DROP (3-shot) | 0.81 |