---
license: apache-2.0
library_name: peft
tags:
- axolotl
- generated_from_trainer
base_model: EleutherAI/pythia-410m-deduped
model-index:
- name: 2bc524d3-39c3-413f-86ea-7a7851e7528b
results: []
---
[
](https://github.com/OpenAccess-AI-Collective/axolotl)
See axolotl config
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: EleutherAI/pythia-410m-deduped
bf16: auto
dataset_prepared_path: null
datasets:
- data_files:
- adcc810a6ceb3267_train_data.json
ds_type: json
format: custom
path: adcc810a6ceb3267_train_data.json
type:
field: null
field_input: domain
field_instruction: sentence
field_output: triples
field_system: null
format: null
no_input_format: null
system_format: '{system}'
system_prompt: ''
early_stopping_patience: null
evals_per_epoch: 2
gradient_accumulation_steps: 1
group_by_length: false
hub_model_id: taopanda-1/2bc524d3-39c3-413f-86ea-7a7851e7528b
learning_rate: 1.0e-05
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: true
lora_model_dir: null
lora_r: 16
lora_target_linear: null
lora_target_modules:
- query_key_value
micro_batch_size: 4
num_epochs: 1
output_dir: ./outputs/lora-alpaca-pythia/taopanda-1_6b76e44c-62d5-4a35-a480-c808b829ee0b
resume_from_checkpoint: null
seed: 96247
sequence_len: 512
special_tokens:
pad_token: <|endoftext|>
tf32: true
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: fatcat87-taopanda
wandb_log_model: null
wandb_mode: online
wandb_name: taopanda-1_6b76e44c-62d5-4a35-a480-c808b829ee0b
wandb_project: subnet56
wandb_runid: taopanda-1_6b76e44c-62d5-4a35-a480-c808b829ee0b
wandb_watch: null
weight_decay: 0.1
```
[
](https://wandb.ai/fatcat87-taopanda/subnet56/runs/qpp21cc7)
# 2bc524d3-39c3-413f-86ea-7a7851e7528b
This model is a fine-tuned version of [EleutherAI/pythia-410m-deduped](https://huggingface.co/EleutherAI/pythia-410m-deduped) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4366
## 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: 4
- eval_batch_size: 4
- seed: 96247
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 16
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 23
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 3.7662 | 0.0013 | 1 | 3.5014 |
| 1.521 | 0.5006 | 399 | 1.4366 |
### Framework versions
- PEFT 0.11.1
- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1