TinyLlama-3T-1.1bee
A grand successor to the original. This one has the following improvements:
- start from finished 3T TinyLlama
- vastly improved and expanded SoTA beekeeping dataset
Model description
This model is a fine-tuned version of TinyLlama-1.1b-3T on the BEE-spoke-data/bees-internal dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1640
- Accuracy: 0.5406
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 2
- seed: 13707
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 2.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 2.4432 | 0.19 | 50 | 2.3850 | 0.5033 |
| 2.3655 | 0.39 | 100 | 2.3124 | 0.5129 |
| 2.374 | 0.58 | 150 | 2.2588 | 0.5215 |
| 2.3558 | 0.78 | 200 | 2.2132 | 0.5291 |
| 2.2677 | 0.97 | 250 | 2.1828 | 0.5348 |
| 2.0701 | 1.17 | 300 | 2.1788 | 0.5373 |
| 2.0766 | 1.36 | 350 | 2.1673 | 0.5398 |
| 2.0669 | 1.56 | 400 | 2.1651 | 0.5402 |
| 2.0314 | 1.75 | 450 | 2.1641 | 0.5406 |
| 2.0281 | 1.95 | 500 | 2.1639 | 0.5407 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0
- Datasets 2.16.1
- Tokenizers 0.15.0
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 36.46 |
| AI2 Reasoning Challenge (25-Shot) | 33.79 |
| HellaSwag (10-Shot) | 60.29 |
| MMLU (5-Shot) | 25.86 |
| TruthfulQA (0-shot) | 38.13 |
| Winogrande (5-shot) | 60.22 |
| GSM8k (5-shot) | 0.45 |