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Aura-MoEv2

Text Generation
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Aura-MoEv2 / README.md
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metadata
library_name: transformers
base_model: jeiku/MoEv2
tags:
  - axolotl
  - generated_from_trainer
datasets:
  - FourOhFour/RP_Phase
  - jeiku/Writing
model-index:
  - name: Aura-MoEv2
    results: []

Built with Axolotl

See axolotl config

axolotl version: 0.6.0

base_model: jeiku/MoEv2
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: FourOhFour/RP_Phase
    type: chat_template
    chat_template: chatml
    roles_to_train: ["gpt"]
    field_messages: conversations
    message_field_role: from
    message_field_content: value
    train_on_eos: turn
  - path: jeiku/Writing
    type: completion
    field: text

chat_template: chatml

shuffle_merged_datasets: true
dataset_prepared_path:
val_set_size: 0.01
output_dir: ./output/out

hub_model_id: jeiku/Aura-MoEv2
hub_strategy: "all_checkpoints"
push_dataset_to_hub:
hf_use_auth_token: true

sequence_len: 8192
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len:

wandb_project: Aura-MoEv2
wandb_entity:
wandb_watch:
wandb_name: Aura-MoEv2
wandb_log_model:

gradient_accumulation_steps: 16
micro_batch_size: 2
num_epochs: 2
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 0.00005

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

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 10
evals_per_epoch: 2
eval_table_size:
eval_max_new_tokens:
saves_per_epoch: 1
debug:
deepspeed: 
weight_decay: 0.05
fsdp:
fsdp_config:
special_tokens:
  pad_token: <|finetune_right_pad_id|>

Aura-MoEv2

This model is a fine-tuned version of jeiku/MoEv2 on the FourOhFour/RP_Phase and the jeiku/Writing datasets. It achieves the following results on the evaluation set:

  • Loss: 1.7106

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: 5e-05
  • train_batch_size: 2
  • eval_batch_size: 2
  • seed: 42
  • gradient_accumulation_steps: 16
  • total_train_batch_size: 32
  • optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT 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: 10
  • num_epochs: 2

Training results

Training Loss Epoch Step Validation Loss
29.5342 0.0038 1 1.8693
27.8562 0.4990 130 1.7601
26.632 0.9981 260 1.6990
21.9675 1.4952 390 1.7117
21.648 1.9942 520 1.7106

Framework versions

  • Transformers 4.47.0
  • Pytorch 2.3.1+cu121
  • Datasets 3.1.0
  • Tokenizers 0.21.0