---
library_name: transformers
tags:
- generated_from_trainer
datasets:
- gs://fine-tuning-634768b5-ef69-4ea6-9fd2-c9379a5ee381/copymediadatatask/execution_artifacts/clean_train.jsonl
model-index:
- name: tmp/output_dir/gcs/fine-tuning-634768b5-ef69-4ea6-9fd2-c9379a5ee381/postprocess/node-0/checkpoints/final
  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: `0.13.0.dev0`
```yaml
base_model: gs://vertex-model-garden-restricted-us/gemma3/gemma-3-12b-it

# gemma3 doesn't seem to play nice with ddp
ddp_find_unused_parameters: true
experimental_skip_move_to_device: true  # prevent OOM by NOT putting model to GPU before sharding

plugins:
- axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
- axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true

chat_template: gemma3
eot_tokens:
- <end_of_turn>

dataset_prepared_path: last_run_prepared
output_dir: /workspace/outputs/out

sequence_len: 8192
sample_packing: true
eval_sample_packing: false

use_kernels: true

micro_batch_size: 2
gradient_accumulation_steps: 1
num_epochs: 3
optimizer: adamw_torch_fused
learning_rate: 1e-5

lr_scheduler: cosine

bf16: true
tf32: true
logging_steps: 1
flash_attention: true

gradient_checkpointing: true
activation_offloading: true

val_set_size: 0
eval_strategy: "epoch"
save_strategy: 'no'
include_tokens_per_second: true
save_safetensors: true
use_tensorboard: true

fsdp_version: 1
fsdp_config:
  fsdp_limit_all_gathers: true
  fsdp_sync_module_states: true
  fsdp_offload_params: true
  fsdp_use_orig_params: false
  fsdp_cpu_ram_efficient_loading: true
  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
  fsdp_transformer_layer_cls_to_wrap: Gemma3DecoderLayer
  fsdp_state_dict_type: SHARDED_STATE_DICT
  fsdp_sharding_strategy: SHARD_GRAD_OP
  fsdp_backward_prefetch: BACKWARD_PRE
  final_state_dict_type: FULL_STATE_DICT

```

</details><br>

# tmp/output_dir/gcs/fine-tuning-634768b5-ef69-4ea6-9fd2-c9379a5ee381/postprocess/node-0/checkpoints/final

This model was trained from scratch on the gs://fine-tuning-634768b5-ef69-4ea6-9fd2-c9379a5ee381/copymediadatatask/execution_artifacts/clean_train.jsonl 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: 1e-07
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 16
- total_eval_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 20
- training_steps: 675

### Training results



### Framework versions

- Transformers 4.55.4
- Pytorch 2.7.1+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4