generated_from_trainer base_model:dhmeltzer/llama-7b-SFT_ds_eli5_1024_r_64_alpha_16_merged region:us
---
base_model: dhmeltzer/llama-7b-SFT_ds_eli5_1024_r_64_alpha_16_merged
tags:
- generated_from_trainer
model-index:
- name: llama-7b-SFT-qlora-eli5_DPO_ds_RM_top_2_1024_r_64_alpha_16
  results: []
---

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# llama-7b-SFT-qlora-eli5_DPO_ds_RM_top_2_1024_r_64_alpha_16

This model is a fine-tuned version of [dhmeltzer/llama-7b-SFT_ds_eli5_1024_r_64_alpha_16_merged](https://huggingface.co/dhmeltzer/llama-7b-SFT_ds_eli5_1024_r_64_alpha_16_merged) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6646
- Rewards/chosen: -0.0509
- Rewards/rejected: -0.1595
- Rewards/accuracies: 0.5955
- Rewards/margins: 0.1086
- Logps/rejected: -205.7926
- Logps/chosen: -209.8076
- Logits/rejected: 1.2037
- Logits/chosen: 1.2178

## 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: 0.0002
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.6921        | 0.1   | 19   | 0.6816          | -0.1096        | -0.1495          | 0.5619             | 0.0399          | -205.6920      | -210.3942    | 1.2101          | 1.2240        |
| 0.6729        | 0.21  | 38   | 0.6800          | -0.3511        | -0.4714          | 0.5670             | 0.1204          | -208.9117      | -212.8093    | 1.1699          | 1.1829        |
| 0.6815        | 0.31  | 57   | 0.6718          | -0.1438        | -0.2304          | 0.5850             | 0.0866          | -206.5014      | -210.7368    | 1.1796          | 1.1924        |
| 0.6656        | 0.42  | 76   | 0.6670          | -0.1608        | -0.2728          | 0.6017             | 0.1120          | -206.9256      | -210.9071    | 1.1690          | 1.1824        |
| 0.6735        | 0.52  | 95   | 0.6656          | -0.0713        | -0.1981          | 0.5948             | 0.1268          | -206.1783      | -210.0114    | 1.1820          | 1.1939        |
| 0.6715        | 0.63  | 114  | 0.6672          | -0.0590        | -0.1724          | 0.5839             | 0.1134          | -205.9213      | -209.8885    | 1.2077          | 1.2215        |
| 0.6722        | 0.73  | 133  | 0.6659          | -0.0568        | -0.1635          | 0.5873             | 0.1067          | -205.8329      | -209.8666    | 1.2080          | 1.2218        |
| 0.6682        | 0.84  | 152  | 0.6646          | -0.0509        | -0.1595          | 0.5955             | 0.1086          | -205.7926      | -209.8076    | 1.2037          | 1.2178        |
| 0.673         | 0.94  | 171  | 0.6652          | -0.0532        | -0.1609          | 0.5960             | 0.1077          | -205.8064      | -209.8306    | 1.1965          | 1.2099        |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
