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---
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
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