---
library_name: transformers
license: llama3
base_model: meta-llama/Meta-Llama-3-8B-Instruct
tags:
- alignment-handbook
- generated_from_trainer
datasets:
- simonycl/Meta-Llama-3-8B-Instruct-metamath-rm-annotate
model-index:
- name: llama-3-8b-instruct-metamath-armorm
  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-3-8b-instruct-metamath-armorm

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 simonycl/Meta-Llama-3-8B-Instruct-metamath-rm-annotate dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1754
- Rewards/chosen: -4.0084
- Rewards/rejected: -10.7133
- Rewards/accuracies: 0.9260
- Rewards/margins: 6.7050
- Logps/rejected: -1190.5024
- Logps/chosen: -493.8166
- Logits/rejected: -0.1943
- Logits/chosen: -0.5449

## 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: 1
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 32
- total_train_batch_size: 128
- total_eval_batch_size: 8
- 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 |
|:-------------:|:------:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.1771        | 0.7881 | 400  | 0.1754          | -4.0084        | -10.7133         | 0.9260             | 6.7050          | -1190.5024     | -493.8166    | -0.1943         | -0.5449       |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1