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pythia-160m-storytelling

Text Generation
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gpt_neox
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metadata
base_model: EleutherAI/pythia-160m-deduped
library_name: peft
license: apache-2.0
tags:
  - axolotl
  - generated_from_trainer
model-index:
  - name: pythia-160m-storytelling
    results: []

Built with Axolotl

See axolotl config

axolotl version: 0.4.1

base_model: EleutherAI/pythia-160m-deduped
load_in_8bit: false
datasets:
  - path: jtatman/storywriting_combined_instruct
    type: alpaca
dataset_prepared_path: ds-storytelling
val_set_size: 0.05
adapter: lora
lora_model_dir: 
sequence_len: 2048
lora_r: 16
lora_alpha: 64
lora_dropout: 0.05
lora_target_modules:
  - query_key_value
lora_target_linear: 
lora_fan_in_fan_out: true  # pythia/GPTNeoX lora specific
wandb_project: pythia
wandb_entity:
wandb_watch:
wandb_name: pythia-160m-storytelling
wandb_log_model:
output_dir: ./outputs/lora-alpaca-pythia-160m-storytelling
gradient_accumulation_steps: 16
micro_batch_size: 1
num_epochs: 5
learning_rate: 0.0006
lr_scheduler: cosine_with_restarts
#cosine_min_lr_ratio: 0.1
train_on_inputs: false
group_by_length: false
#bf16: auto
#fp16: true
#tf32: false
float16: true
flash_attn: 
xformers_attention: true
optimizer: paged_adamw_8bit
gpu_memory_limit: 8GiB
hub_model_id: jtatman/pythia-160m-storytelling 
lora_on_cpu: true
early_stopping_patience: 3
#resume_from_checkpoint: outputs/lora-alpaca-pythia-125m/checkpoint-51040
auto_resume_from_checkpoints: true
local_rank:
weight_decay: 0.1
chat_template: inst
#evals_per_epoch: 4
eval_steps: 2000
logging_steps: 1
save_steps: 2000
save_total_limit: 5
warmup_steps: 1000

pythia-160m-storytelling

This model is a fine-tuned version of EleutherAI/pythia-160m-deduped on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 10.3843

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: 0.0006
  • train_batch_size: 1
  • eval_batch_size: 1
  • seed: 42
  • gradient_accumulation_steps: 16
  • total_train_batch_size: 16
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine_with_restarts
  • lr_scheduler_warmup_steps: 1000
  • num_epochs: 5

Training results

Training Loss Epoch Step Validation Loss
5.4891 0.0012 1 4.5640
8.4799 2.4467 2000 9.1436
9.9198 4.8944 4000 10.3843

Framework versions

  • PEFT 0.11.1
  • Transformers 4.41.2
  • Pytorch 2.3.0+cu121
  • Datasets 2.19.1
  • Tokenizers 0.19.1