115 lines
3.7 KiB
Markdown
115 lines
3.7 KiB
Markdown
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---
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tags:
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- generated_from_trainer
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metrics:
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- accuracy
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inference:
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parameters:
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max_new_tokens: 64
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do_sample: true
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repetition_penalty: 1.1
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no_repeat_ngram_size: 5
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guidance_scale: 1.01
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eta_cutoff: 0.001
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widget:
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- text: My name is El Microondas the Wise and
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example_title: El Microondas
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- text: A meme is
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example_title: meme
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- text: >-
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Barack Obama nominated Hilary Clinton as his secretary of state on Monday.
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He chose her because she had
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example_title: Coreference resolution
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- text: >-
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On a shelf, there are five books: a gray book, a red book, a purple book, a
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blue book, and a black book
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example_title: Logic puzzles
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- text: >-
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The two men running to become New York City's next mayor will face off in
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their first debate Wednesday night
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example_title: Reading comprehension
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datasets:
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- pszemraj/simple_wikipedia_LM
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pipeline_tag: text-generation
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license: apache-2.0
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# pythia-31m-simplewiki-2048
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This was initialized from random weights based on the config of [EleutherAI/pythia-31m](https://huggingface.co/EleutherAI/pythia-31m) and trained on `pszemraj/simple_wikipedia_LM` for 3 epochs.
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It achieves the following results on the evaluation set:
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- Loss: 3.6874
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- Accuracy: 0.4105
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## Model description
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More information needed
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## Intended uses & limitations
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This is a baseline for comparison to other models.
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.0005
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- train_batch_size: 1
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- eval_batch_size: 1
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- seed: 80085
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- gradient_accumulation_steps: 64
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- total_train_batch_size: 64
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- optimizer: Adam with betas=(0.9,0.99) and epsilon=1e-07
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- lr_scheduler_type: inverse_sqrt
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- lr_scheduler_warmup_ratio: 0.05
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- num_epochs: 3.0
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|
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| 6.0657 | 0.22 | 100 | 5.6210 | 0.2414 |
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| 5.2447 | 0.45 | 200 | 4.9316 | 0.3054 |
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| 4.8397 | 0.67 | 300 | 4.6011 | 0.3343 |
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| 4.7933 | 0.9 | 400 | 4.3878 | 0.3530 |
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| 4.274 | 1.12 | 500 | 4.2352 | 0.3646 |
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| 4.4867 | 1.35 | 600 | 4.1224 | 0.3723 |
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| 4.3434 | 1.57 | 700 | 4.0282 | 0.3791 |
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| 4.1857 | 1.8 | 800 | 3.9552 | 0.3841 |
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| 4.229 | 2.02 | 900 | 3.8890 | 0.3909 |
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| 3.9189 | 2.25 | 1000 | 3.8301 | 0.3967 |
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| 4.084 | 2.47 | 1100 | 3.7782 | 0.4023 |
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| 3.8965 | 2.7 | 1200 | 3.7302 | 0.4069 |
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| 3.915 | 2.92 | 1300 | 3.6874 | 0.4105 |
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### Framework versions
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- Transformers 4.33.1
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- Pytorch 2.2.0.dev20230907+cu118
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- Datasets 2.14.5
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- Tokenizers 0.13.3
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_pszemraj__pythia-31m-simplewiki-2048)
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| Metric | Value |
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|-----------------------|---------------------------|
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| Avg. | 24.35 |
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| ARC (25-shot) | 22.18 |
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| HellaSwag (10-shot) | 25.55 |
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| MMLU (5-shot) | 23.12 |
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| TruthfulQA (0-shot) | 49.37 |
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| Winogrande (5-shot) | 49.41 |
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| GSM8K (5-shot) | 0.0 |
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| DROP (3-shot) | 0.81 |
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