---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen3-8B
tags:
- axolotl
- base_model:adapter:Qwen/Qwen3-8B
- lora
- transformers
datasets:
- trillionlabs/android_control_ER_index_1000
pipeline_tag: text-generation
model-index:
- name: android_control_ER_index_1000
  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: `0.12.2`
```yaml
base_model: Qwen/Qwen3-8B

strict: false

chat_template: tokenizer_default
datasets:
  - path: trillionlabs/android_control_ER_index_1000
    type: chat_template
    split: train
    field_messages: messages

adapter: lora
lora_model_dir:
lora_r: 32
lora_alpha: 64
lora_dropout: 0.05
lora_target_linear: true

dataset_prepared_path: datasets/android_control_ER_index_1000_prepared
val_set_size: 0.01
output_dir: ./outputs/sft_android_control_ER_index_1000
hub_model_id: trillionlabs/android_control_ER_index_1000

sequence_len: 6144
sample_packing: false
pad_to_sequence_len: false

wandb_project: axolotl
wandb_entity: suyeong_korea_univ-korea-university
wandb_name: android_control_ER_index_1000

gradient_accumulation_steps: 2
micro_batch_size: 2
num_epochs: 2
optimizer: adamw_torch_fused
lr_scheduler: cosine
learning_rate: 3e-6

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
xformers_attention:
flash_attention: true
max_prompt_len: 6144


warmup_steps: 50
evals_per_epoch: 0
eval_table_size:
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
  - full_shard
  - auto_wrap
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_state_dict_type: FULL_STATE_DICT
  fsdp_sharding_strategy: FULL_SHARD
  fsdp_backward_prefetch: BACKWARD_PRE
special_tokens:
  pad_token: <|pad_token|>
  eos_token: <|im_end|>

seed: 11
```

</details><br>

# android_control_ER_index_1000

This model is a fine-tuned version of [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) on the trillionlabs/android_control_ER_index_1000 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: 3e-06
- train_batch_size: 2
- eval_batch_size: 2
- seed: 11
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- total_eval_batch_size: 8
- 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: 50
- training_steps: 689

### Training results



### Framework versions

- PEFT 0.17.0
- Transformers 4.56.0
- Pytorch 2.7.1+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0