---
license: llama3.1
base_model: meta-llama/Llama-3.1-8B
tags:
- generated_from_trainer
model-index:
- name: Llama-3.1-8B-dpo-10k
  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. -->

# Llama-3.1-8B-dpo-10k

This model is a fine-tuned version of [meta-llama/Llama-3.1-8B](https://huggingface.co./meta-llama/Llama-3.1-8B) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7481
- Rewards/real: 0.4771
- Rewards/generated: 0.1826
- Rewards/accuracies: 0.6538
- Rewards/margins: 0.2945
- Logps/generated: -119.3277
- Logps/real: -137.5565
- Logits/generated: -1.4543
- Logits/real: -1.5698

## 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: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- total_eval_batch_size: 16
- 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 |
|:-------------:|:------:|:----:|:---------------:|:------------:|:-----------------:|:------------------:|:---------------:|:---------------:|:----------:|:----------------:|:-----------:|
| 0.3318        | 0.6494 | 200  | 0.7481          | 0.4771       | 0.1826            | 0.6538             | 0.2945          | -119.3277       | -137.5565  | -1.4543          | -1.5698     |


### Framework versions

- Transformers 4.43.3
- Pytorch 2.2.2+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1