---
base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
datasets:
- africa-intelligence/yahma-alpaca-cleaned-tn
- africa-intelligence/yahma-alpaca-cleaned-xh
- africa-intelligence/yahma-alpaca-cleaned-zu
- africa-intelligence/yahma-alpaca-cleaned-af
- africa-intelligence/yahma-alpaca-cleaned-en
- africa-intelligence/yahma-alpaca-cleaned-nso
library_name: peft
license: llama3.1
tags:
- alignment-handbook
- trl
- sft
- generated_from_trainer
model-index:
- name: llama-8b-south-africa
  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-8b-south-africa

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 africa-intelligence/yahma-alpaca-cleaned-tn, the africa-intelligence/yahma-alpaca-cleaned-xh, the africa-intelligence/yahma-alpaca-cleaned-zu, the africa-intelligence/yahma-alpaca-cleaned-af, the africa-intelligence/yahma-alpaca-cleaned-en and the africa-intelligence/yahma-alpaca-cleaned-nso datasets.
It achieves the following results on the evaluation set:
- Loss: 1.0571

## 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: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 2
- total_train_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 |
|:-------------:|:------:|:----:|:---------------:|
| 1.0962        | 0.9999 | 5596 | 1.0571          |


### Framework versions

- PEFT 0.12.0
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1