---
license: mit
base_model: microsoft/Phi-3-small-8k-instruct
tags:
- alignment-handbook
- generated_from_trainer
datasets:
- AmberYifan/spin-v
model-index:
- name: phi3-spin-zephyr-data
  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. -->

# phi3-spin-zephyr-data

This model is a fine-tuned version of [microsoft/Phi-3-small-8k-instruct](https://huggingface.co/microsoft/Phi-3-small-8k-instruct) on the AmberYifan/spin-v dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1680
- Rewards/real: -6.9251
- Rewards/generated: -22.7311
- Rewards/accuracies: 0.9443
- Rewards/margins: 15.8060
- Logps/generated: -518.7460
- Logps/real: -321.6379
- Logits/generated: -inf
- Logits/real: -inf

## 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: 5e-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 32
- total_eval_batch_size: 32
- 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

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rewards/real | Rewards/generated | Rewards/accuracies | Rewards/margins | Logps/generated | Logps/real | Logits/generated | Logits/real |
|:-------------:|:-----:|:----:|:---------------:|:------------:|:-----------------:|:------------------:|:---------------:|:---------------:|:----------:|:----------------:|:-----------:|
| 0.2807        | 0.64  | 500  | 0.1680          | -6.9251      | -22.7311          | 0.9443             | 15.8060         | -518.7460       | -321.6379  | -inf             | -inf        |


### Framework versions

- Transformers 4.37.0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2