---
library_name: transformers
license: other
base_model: meta-llama/Llama-3.1-8B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
metrics:
- val accuracy
model-index:
- name: reward
  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. -->

# reward

This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) on the gsm8k_llama3.1-8B_128_1ep dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2467
- val Accuracy: 0.8810

## 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-05
- train_batch_size: 1
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- total_eval_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 1.0

### Training results

| Training Loss | Epoch  | Step | Validation Loss | val Accuracy |
|:-------------:|:------:|:----:|:---------------:|:------------:|
| 0.609         | 0.0856 | 5    | 0.4890          | 0.8135       |
| 0.3044        | 0.1711 | 10   | 0.2622          | 0.9204       |
| 0.3091        | 0.2567 | 15   | 0.1574          | 0.9060       |
| 0.2377        | 0.3422 | 20   | 0.2161          | 0.9090       |
| 0.2227        | 0.4278 | 25   | 0.2810          | 0.8696       |
| 0.3034        | 0.5134 | 30   | 0.2796          | 0.8832       |
| 0.2101        | 0.5989 | 35   | 0.2074          | 0.9022       |
| 0.2027        | 0.6845 | 40   | 0.1866          | 0.9075       |
| 0.2683        | 0.7701 | 45   | 0.2167          | 0.8976       |
| 0.1873        | 0.8556 | 50   | 0.2340          | 0.8878       |
| 0.2984        | 0.9412 | 55   | 0.2451          | 0.8825       |


### Framework versions

- Transformers 4.46.1
- Pytorch 2.5.1
- Datasets 3.1.0
- Tokenizers 0.20.1