---
library_name: transformers
license: other
base_model: swiss-ai/Apertus-8B-2509
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: Apertus-feedback
  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. -->

# Apertus-feedback

This model is a fine-tuned version of [/mnt/task_runtime/models/Apertus-8B-cpt](https://huggingface.co//mnt/task_runtime/models/Apertus-8B-cpt) on the sft dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0303

## 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: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 7
- total_train_batch_size: 14
- total_eval_batch_size: 14
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 1.0

### Training results

| Training Loss | Epoch  | Step  | Validation Loss |
|:-------------:|:------:|:-----:|:---------------:|
| 0.0609        | 0.0777 | 1000  | 0.0622          |
| 0.0428        | 0.1553 | 2000  | 0.0455          |
| 0.04          | 0.2330 | 3000  | 0.0402          |
| 0.0349        | 0.3107 | 4000  | 0.0371          |
| 0.0384        | 0.3883 | 5000  | 0.0352          |
| 0.0319        | 0.4660 | 6000  | 0.0337          |
| 0.0313        | 0.5437 | 7000  | 0.0326          |
| 0.0312        | 0.6214 | 8000  | 0.0318          |
| 0.0307        | 0.6990 | 9000  | 0.0312          |
| 0.0303        | 0.7767 | 10000 | 0.0308          |
| 0.0283        | 0.8544 | 11000 | 0.0305          |
| 0.0279        | 0.9320 | 12000 | 0.0304          |


### Framework versions

- Transformers 4.57.6
- Pytorch 2.9.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2