---
library_name: transformers
base_model: AlexHung29629/FormoMouse123
tags:
- generated_from_trainer
model-index:
- name: out_1
  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. -->

[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.9.2`
```yaml
base_model: AlexHung29629/FormoMouse123

plugins:
  - axolotl.integrations.liger.LigerPlugin
liger_rope: false
liger_rms_norm: true
liger_glu_activation: true
liger_fused_linear_cross_entropy: true

unfrozen_parameters:
  - model.lm_head.weight
  - model.embed_tokens.weight

pretraining_dataset:
  - path: AlexHung29629/para_pat_text
    split: train
    text_column: text
    type: pretrain
  - path: AlexHung29629/urban_dictionary
    split: train
    text_column: text
    type: pretrain

dataset_prepared_path: data_prep_1
val_set_size: 0.0
output_dir: ./out_1

shuffle_merged_datasets: true

sequence_len: 2048
pretraining_sample_concatenation: true
#sample_packing: true
#eval_sample_packing: false
pad_to_sequence_len: true

use_tensorboard: true

gradient_accumulation_steps: 1
micro_batch_size: 1
num_epochs: 2
max_steps: 10000
save_steps: 1000
save_total_limit: 1
save_only_model: true
optimizer: ao_adamw_fp8
lr_scheduler: cosine
learning_rate: 2e-4
max_grad_norm: 1.0

bfloat16: true

gradient_checkpointing: true

logging_steps: 1
torch_compile: false
sdp_attention: true


warmup_ratio: 0.1
weight_decay: 0.1

```

</details><br>

# out_1

This model is a fine-tuned version of [AlexHung29629/FormoMouse123](https://huggingface.co/AlexHung29629/FormoMouse123) on an unknown dataset.

## 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: 1
- eval_batch_size: 1
- seed: 42
- 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_steps: 1000
- training_steps: 10000

### Training results



### Framework versions

- Transformers 4.51.3
- Pytorch 2.7.0+cu128
- Datasets 3.5.1
- Tokenizers 0.21.1