---
library_name: transformers
license: llama3
base_model: meta-llama/Meta-Llama-3-8B-Instruct
tags:
- alignment-handbook
- trl
- simpo
- generated_from_trainer
- trl
- simpo
- generated_from_trainer
datasets:
- yakazimir/llama3-ultrafeedback-armorm
model-index:
- name: llama3instruct_-l5-10-0_3-1e-6-2_best
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# llama3instruct_-l5-10-0_3-1e-6-2_best

This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the yakazimir/llama3-ultrafeedback-armorm dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2685
- Rewards/chosen: -8.0711
- Rewards/rejected: -19.7238
- Rewards/accuracies: 0.8675
- Rewards/margins: 11.6527
- Logps/rejected: -1.9724
- Logps/chosen: -0.8071
- Logits/rejected: -1.3327
- Logits/chosen: -1.4140

## 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: 1e-06
- train_batch_size: 2
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2.0

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:------:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 1.4299        | 0.8743 | 400  | 1.4682          | -8.3837        | -17.5861         | 0.8705             | 9.2024          | -1.7586        | -0.8384      | -1.2770         | -1.3300       |
| 0.7858        | 1.7486 | 800  | 1.2716          | -7.9331        | -19.2874         | 0.8614             | 11.3543         | -1.9287        | -0.7933      | -1.2977         | -1.3755       |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1