|
--- |
|
license: mit |
|
base_model: microsoft/phi-1_5 |
|
tags: |
|
- alignment-handbook |
|
- trl |
|
- sft |
|
- generated_from_trainer |
|
- trl |
|
- sft |
|
- generated_from_trainer |
|
datasets: |
|
- HuggingFaceH4/ultrachat_200k |
|
model-index: |
|
- name: phi-1_5_sft |
|
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. --> |
|
|
|
# phi-1_5_sft |
|
|
|
This model is a fine-tuned version of [microsoft/phi-1_5](https://huggingface.co./microsoft/phi-1_5) on the HuggingFaceH4/ultrachat_200k dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 1.2542 |
|
|
|
## 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: 2e-05 |
|
- train_batch_size: 16 |
|
- eval_batch_size: 8 |
|
- seed: 42 |
|
- distributed_type: multi-GPU |
|
- num_devices: 4 |
|
- gradient_accumulation_steps: 2 |
|
- total_train_batch_size: 128 |
|
- total_eval_batch_size: 32 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: cosine |
|
- lr_scheduler_warmup_steps: 120 |
|
- num_epochs: 3 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | |
|
|:-------------:|:-----:|:----:|:---------------:| |
|
| 1.3099 | 0.1 | 100 | 1.3398 | |
|
| 1.3131 | 0.2 | 200 | 1.3159 | |
|
| 1.3009 | 0.3 | 300 | 1.3046 | |
|
| 1.2915 | 0.4 | 400 | 1.2967 | |
|
| 1.2714 | 0.5 | 500 | 1.2906 | |
|
| 1.2811 | 0.6 | 600 | 1.2854 | |
|
| 1.2621 | 0.7 | 700 | 1.2807 | |
|
| 1.2406 | 0.8 | 800 | 1.2767 | |
|
| 1.2371 | 0.9 | 900 | 1.2731 | |
|
| 1.2547 | 1.0 | 1000 | 1.2699 | |
|
| 1.2085 | 1.1 | 1100 | 1.2693 | |
|
| 1.2253 | 1.2 | 1200 | 1.2669 | |
|
| 1.215 | 1.3 | 1300 | 1.2649 | |
|
| 1.2103 | 1.4 | 1400 | 1.2630 | |
|
| 1.2081 | 1.5 | 1500 | 1.2612 | |
|
| 1.2033 | 1.6 | 1600 | 1.2597 | |
|
| 1.2307 | 1.7 | 1700 | 1.2582 | |
|
| 1.2038 | 1.8 | 1800 | 1.2568 | |
|
| 1.2014 | 1.9 | 1900 | 1.2557 | |
|
| 1.188 | 2.0 | 2000 | 1.2546 | |
|
| 1.1473 | 2.1 | 2100 | 1.2563 | |
|
| 1.1872 | 2.2 | 2200 | 1.2559 | |
|
| 1.2086 | 2.3 | 2300 | 1.2553 | |
|
| 1.1896 | 2.4 | 2400 | 1.2550 | |
|
| 1.1733 | 2.5 | 2500 | 1.2548 | |
|
| 1.1665 | 2.6 | 2600 | 1.2544 | |
|
| 1.1499 | 2.7 | 2700 | 1.2543 | |
|
| 1.1779 | 2.8 | 2800 | 1.2542 | |
|
| 1.1746 | 2.9 | 2900 | 1.2542 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.37.0 |
|
- Pytorch 2.1.2+cu118 |
|
- Datasets 2.16.1 |
|
- Tokenizers 0.15.0 |
|
|
