|
--- |
|
license: mit |
|
base_model: microsoft/Phi-3-mini-4k-instruct |
|
tags: |
|
- alignment-handbook |
|
- generated_from_trainer |
|
datasets: |
|
- trl-lib/kto-mix-14k |
|
- chaoweihuang/lf-response-phi3-f1_100_0.7-fg0.5 |
|
model-index: |
|
- name: kto-mix-14k-lf-response-phi3-f1_100_0.7-fg0.5-kto-fg-fgudw4.0 |
|
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. --> |
|
|
|
# FactAlign-Phi-3-Mini |
|
|
|
This model is aligned with our **FactAlign** framework for improved long-form factuality, from [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co./microsoft/Phi-3-mini-4k-instruct). |
|
|
|
For more information, please refer to our paper: [FactAlign: Long-form Factuality Alignment of Large Language Models](https://huggingface.co./papers/2410.01691). |
|
|
|
|
|
|
|
|
|
## Model description |
|
|
|
More information needed |
|
|
|
## Intended uses & limitations |
|
|
|
More information needed |
|
|
|
## Training and evaluation data |
|
|
|
This model is a fine-tuned version of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co./microsoft/Phi-3-mini-4k-instruct) on the trl-lib/kto-mix-14k and the chaoweihuang/lf-response-phi3-f1_100_0.7-fg0.5 datasets. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.4815 |
|
- Rewards/chosen: -0.6601 |
|
- Logps/chosen: -299.7121 |
|
- Rewards/rejected: -2.6435 |
|
- Logps/rejected: -364.3744 |
|
- Rewards/margins: 1.9834 |
|
- Kl: 0.0081 |
|
- Fg Kl: nan |
|
- Fg Rewards/chosen Sum: 0.0694 |
|
- Fg Logps/policy Chosen: -15.2781 |
|
- Fg Logps/reference Chosen: -14.9295 |
|
- Count/fg Chosen: 16.0137 |
|
- Fg Rewards/rejected Sum: -0.3623 |
|
- Fg Logps/policy Rejected: -19.6552 |
|
- Fg Logps/reference Rejected: -18.7868 |
|
- Count/fg Rejected: 4.0824 |
|
- Fg Logps/policy Kl: -21.1260 |
|
- Fg Logps/reference Kl: -20.2070 |
|
- Fg Loss: 0.7365 |
|
|
|
## Training procedure |
|
|
|
### Training hyperparameters |
|
|
|
The following hyperparameters were used during training: |
|
- learning_rate: 5e-07 |
|
- train_batch_size: 1 |
|
- eval_batch_size: 1 |
|
- seed: 42 |
|
- distributed_type: multi-GPU |
|
- num_devices: 2 |
|
- gradient_accumulation_steps: 8 |
|
- total_train_batch_size: 16 |
|
- total_eval_batch_size: 2 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- lr_scheduler_warmup_ratio: 0.1 |
|
- num_epochs: 1.0 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Logps/chosen | Rewards/rejected | Logps/rejected | Rewards/margins | Kl | Fg Kl | Fg Rewards/chosen Sum | Fg Logps/policy Chosen | Fg Logps/reference Chosen | Count/fg Chosen | Fg Rewards/rejected Sum | Fg Logps/policy Rejected | Fg Logps/reference Rejected | Count/fg Rejected | Fg Logps/policy Kl | Fg Logps/reference Kl | Fg Loss | |
|
|:-------------:|:------:|:----:|:---------------:|:--------------:|:------------:|:----------------:|:--------------:|:---------------:|:------:|:-----:|:---------------------:|:----------------------:|:-------------------------:|:---------------:|:-----------------------:|:------------------------:|:---------------------------:|:-----------------:|:------------------:|:---------------------:|:-------:| |
|
| 0.4495 | 0.4103 | 400 | 0.4978 | -1.0397 | -303.5076 | -2.7182 | -365.1212 | 1.6785 | 0.0054 | nan | -1.3184 | -16.1070 | -14.9295 | 16.0137 | -0.5732 | -20.2671 | -18.7868 | 4.0824 | -21.1826 | -20.2070 | 0.7449 | |
|
| 0.5189 | 0.8206 | 800 | 0.4815 | -0.6601 | -299.7121 | -2.6435 | -364.3744 | 1.9834 | 0.0081 | nan | 0.0694 | -15.2781 | -14.9295 | 16.0137 | -0.3623 | -19.6552 | -18.7868 | 4.0824 | -21.1260 | -20.2070 | 0.7365 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.41.1 |
|
- Pytorch 2.3.0+cu121 |
|
- Datasets 2.19.1 |
|
- Tokenizers 0.19.1 |
|
|
