|
--- |
|
license: other |
|
base_model: meta-llama/Meta-Llama-3-8B |
|
tags: |
|
- llama-factory |
|
- full |
|
- generated_from_trainer |
|
model-index: |
|
- name: C018_random_sample_llama3-8b-base_pretrain_20240504_182259 |
|
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. --> |
|
|
|
# C018_random_sample_llama3-8b-base_pretrain_20240504_182259 |
|
|
|
This model is a fine-tuned version of [/data/pro-align/progressalign/shared_storage/downloaded_models/llama3-8b-base](https://huggingface.co//data/pro-align/progressalign/shared_storage/downloaded_models/llama3-8b-base) on the C018_random_sample_data dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 2.2706 |
|
|
|
## 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: 1.5e-05 |
|
- train_batch_size: 8 |
|
- eval_batch_size: 16 |
|
- seed: 42 |
|
- distributed_type: multi-GPU |
|
- num_devices: 8 |
|
- total_train_batch_size: 64 |
|
- total_eval_batch_size: 128 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: polynomial |
|
- lr_scheduler_warmup_steps: 20 |
|
- num_epochs: 4.0 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | |
|
|:-------------:|:------:|:----:|:---------------:| |
|
| 2.3701 | 0.2186 | 200 | 2.3702 | |
|
| 2.3183 | 0.4372 | 400 | 2.3160 | |
|
| 2.2634 | 0.6557 | 600 | 2.2863 | |
|
| 2.2522 | 0.8743 | 800 | 2.2706 | |
|
| 2.0306 | 1.0929 | 1000 | 2.2777 | |
|
| 2.0095 | 1.3115 | 1200 | 2.2760 | |
|
| 2.0539 | 1.5301 | 1400 | 2.2746 | |
|
| 2.0338 | 1.7486 | 1600 | 2.2743 | |
|
| 2.0648 | 1.9672 | 1800 | 2.2737 | |
|
| 2.0297 | 2.1858 | 2000 | 2.2766 | |
|
| 2.0487 | 2.4044 | 2200 | 2.2767 | |
|
| 2.0329 | 2.6230 | 2400 | 2.2770 | |
|
| 2.0213 | 2.8415 | 2600 | 2.2766 | |
|
| 2.0559 | 3.0601 | 2800 | 2.2771 | |
|
| 2.0543 | 3.2787 | 3000 | 2.2773 | |
|
| 2.0317 | 3.4973 | 3200 | 2.2772 | |
|
| 1.988 | 3.7158 | 3400 | 2.2770 | |
|
| 2.0355 | 3.9344 | 3600 | 2.2772 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.40.1 |
|
- Pytorch 2.3.0 |
|
- Datasets 2.19.0 |
|
- Tokenizers 0.19.1 |
|
|
