|
--- |
|
license: apache-2.0 |
|
base_model: BEE-spoke-data/smol_llama-101M-GQA |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- accuracy |
|
model-index: |
|
- name: smol_llama-101M-GQA-midjourney-messages-cleaned-1024-vN |
|
results: [] |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# smol_llama-101M-GQA-midjourney-messages-cleaned-1024-vN |
|
|
|
This model is a fine-tuned version of [BEE-spoke-data/smol_llama-101M-GQA](https://huggingface.co/BEE-spoke-data/smol_llama-101M-GQA) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 2.8431 |
|
- Accuracy: 0.4682 |
|
|
|
## 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: 0.00025 |
|
- train_batch_size: 4 |
|
- eval_batch_size: 4 |
|
- seed: 17056 |
|
- gradient_accumulation_steps: 16 |
|
- total_train_batch_size: 64 |
|
- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-08 |
|
- lr_scheduler_type: inverse_sqrt |
|
- lr_scheduler_warmup_ratio: 0.05 |
|
- num_epochs: 1.0 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:| |
|
| 3.3031 | 0.03 | 200 | 3.2643 | 0.4169 | |
|
| 3.1762 | 0.06 | 400 | 3.1674 | 0.4247 | |
|
| 3.0914 | 0.08 | 600 | 3.0850 | 0.4359 | |
|
| 3.0384 | 0.11 | 800 | 3.0371 | 0.4419 | |
|
| 3.0235 | 0.14 | 1000 | 3.0057 | 0.4467 | |
|
| 2.9874 | 0.17 | 1200 | 2.9816 | 0.4496 | |
|
| 2.9708 | 0.19 | 1400 | 2.9650 | 0.4518 | |
|
| 2.9796 | 0.22 | 1600 | 2.9487 | 0.4541 | |
|
| 2.9371 | 0.25 | 1800 | 2.9364 | 0.4560 | |
|
| 2.932 | 0.28 | 2000 | 2.9265 | 0.4571 | |
|
| 2.9272 | 0.3 | 2200 | 2.9175 | 0.4580 | |
|
| 2.935 | 0.33 | 2400 | 2.9115 | 0.4591 | |
|
| 2.9074 | 0.36 | 2600 | 2.9038 | 0.4600 | |
|
| 2.9404 | 0.39 | 2800 | 2.8986 | 0.4611 | |
|
| 2.8896 | 0.41 | 3000 | 2.8938 | 0.4617 | |
|
| 2.8946 | 0.44 | 3200 | 2.8893 | 0.4624 | |
|
| 2.9183 | 0.47 | 3400 | 2.8855 | 0.4623 | |
|
| 2.887 | 0.5 | 3600 | 2.8813 | 0.4638 | |
|
| 2.8823 | 0.52 | 3800 | 2.8780 | 0.4638 | |
|
| 2.9171 | 0.55 | 4000 | 2.8744 | 0.4642 | |
|
| 2.8884 | 0.58 | 4200 | 2.8718 | 0.4646 | |
|
| 2.8875 | 0.61 | 4400 | 2.8700 | 0.4651 | |
|
| 2.9121 | 0.63 | 4600 | 2.8668 | 0.4653 | |
|
| 2.8653 | 0.66 | 4800 | 2.8639 | 0.4658 | |
|
| 2.8603 | 0.69 | 5000 | 2.8625 | 0.4659 | |
|
| 2.8489 | 0.72 | 5200 | 2.8598 | 0.4661 | |
|
| 2.8674 | 0.74 | 5400 | 2.8577 | 0.4666 | |
|
| 2.884 | 0.77 | 5600 | 2.8554 | 0.4669 | |
|
| 2.857 | 0.8 | 5800 | 2.8535 | 0.4672 | |
|
| 2.8747 | 0.83 | 6000 | 2.8516 | 0.4673 | |
|
| 2.8809 | 0.86 | 6200 | 2.8501 | 0.4672 | |
|
| 2.8832 | 0.88 | 6400 | 2.8482 | 0.4679 | |
|
| 2.8817 | 0.91 | 6600 | 2.8472 | 0.4681 | |
|
| 2.8813 | 0.94 | 6800 | 2.8457 | 0.4684 | |
|
| 2.8493 | 0.97 | 7000 | 2.8444 | 0.4677 | |
|
| 2.8455 | 0.99 | 7200 | 2.8431 | 0.4682 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.36.0.dev0 |
|
- Pytorch 2.1.0 |
|
- Datasets 2.15.0 |
|
- Tokenizers 0.15.0 |
|
|
