---
library_name: transformers
license: llama3.1
base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
tags:
- alignment-handbook
- trl
- dpo
- generated_from_trainer
- trl
- dpo
- generated_from_trainer
datasets:
- HuggingFaceH4/ultrafeedback_binarized
- tanliboy/orca_dpo_pairs
model-index:
- name: lambda-llama-3-8b-dpo-test-orca
  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. -->

# lambda-llama-3-8b-dpo-test-orca

This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct) on the HuggingFaceH4/ultrafeedback_binarized and the tanliboy/orca_dpo_pairs datasets.
It achieves the following results on the evaluation set:
- Loss: 0.4795
- Rewards/chosen: -1.6860
- Rewards/rejected: -2.8132
- Rewards/accuracies: 0.7259
- Rewards/margins: 1.1272
- Logps/rejected: -645.5051
- Logps/chosen: -549.3651
- Logits/rejected: -2.6630
- Logits/chosen: -2.5985

## 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-07
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- 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_ratio: 0.1
- 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 |
|:-------------:|:------:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.6011        | 0.1744 | 100  | 0.5738          | -0.8770        | -1.2808          | 0.6988             | 0.4038          | -492.2603      | -468.4565    | -2.4544         | -2.4042       |
| 0.5447        | 0.3489 | 200  | 0.5242          | -1.3236        | -2.0879          | 0.7289             | 0.7644          | -572.9752      | -513.1177    | -2.6319         | -2.5732       |
| 0.5173        | 0.5233 | 300  | 0.5003          | -1.6828        | -2.6810          | 0.7259             | 0.9982          | -632.2809      | -549.0404    | -2.6140         | -2.5556       |
| 0.5144        | 0.6978 | 400  | 0.4851          | -1.7107        | -2.8135          | 0.7319             | 1.1028          | -645.5279      | -551.8306    | -2.7027         | -2.6365       |
| 0.5162        | 0.8722 | 500  | 0.4798          | -1.7085        | -2.8440          | 0.7259             | 1.1355          | -648.5815      | -551.6072    | -2.6442         | -2.5812       |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1