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pythia-31m-KI_v1-2048-scratch - GGUF
- Model creator: https://huggingface.co/pszemraj/
- Original model: https://huggingface.co/pszemraj/pythia-31m-KI_v1-2048-scratch/
Original model description:
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 pipeline_tag: text-generation license: apache-2.0 language: - en
pythia-31m-KI_v1-2048-scratch
Initialized from random weights based on config of EleutherAI/pythia-31m, 3 epochs bf16 It achieves the following results on the evaluation set:
- Loss: 4.6160
- Accuracy: 0.2448
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: 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 |
|---|---|---|---|---|
| 6.3874 | 0.16 | 100 | 6.4212 | 0.1487 |
| 5.7088 | 0.32 | 200 | 5.7926 | 0.1725 |
| 5.4575 | 0.48 | 300 | 5.5160 | 0.1903 |
| 5.2451 | 0.64 | 400 | 5.3429 | 0.1995 |
| 5.0954 | 0.8 | 500 | 5.2109 | 0.2059 |
| 5.0358 | 0.96 | 600 | 5.1068 | 0.2123 |
| 4.94 | 1.12 | 700 | 5.0321 | 0.2157 |
| 4.8532 | 1.28 | 800 | 4.9605 | 0.2202 |
| 4.7602 | 1.44 | 900 | 4.9047 | 0.224 |
| 4.6965 | 1.6 | 1000 | 4.8526 | 0.2276 |
| 4.6855 | 1.76 | 1100 | 4.8139 | 0.2300 |
| 4.6573 | 1.91 | 1200 | 4.7739 | 0.2327 |
| 4.5968 | 2.07 | 1300 | 4.7451 | 0.2346 |
| 4.5688 | 2.23 | 1400 | 4.7152 | 0.2370 |
| 4.5205 | 2.39 | 1500 | 4.6842 | 0.2396 |
| 4.5369 | 2.55 | 1600 | 4.6598 | 0.2410 |
| 4.5106 | 2.71 | 1700 | 4.6352 | 0.2433 |
| 4.4375 | 2.87 | 1800 | 4.6160 | 0.2448 |
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 25.21 |
| ARC (25-shot) | 23.12 |
| HellaSwag (10-shot) | 25.23 |
| MMLU (5-shot) | 23.12 |
| TruthfulQA (0-shot) | 51.67 |
| Winogrande (5-shot) | 51.78 |
| GSM8K (5-shot) | 0.0 |
| DROP (3-shot) | 1.52 |
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