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shisa-v1-swallowmx-13a47b

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metadata
license: apache-2.0
base_model: tokyotech-llm/Swallow-MX-8x7b-NVE-v0.1
tags:
  - generated_from_trainer
model-index:
  - name: outputs/basemodel-swallowmx-8x22b
    results: []

Built with Axolotl

See axolotl config

axolotl version: 0.4.0

base_model: tokyotech-llm/Swallow-MX-8x7b-NVE-v0.1
model_type: AutoModelForCausalLM
tokenizer_type: LlamaTokenizer
trust_remote_code: true

load_in_8bit: false
load_in_4bit: false
strict: false

chat_template: inst
datasets:
  - path: augmxnt/ultra-orca-boros-en-ja-v1
    type: sharegpt
dataset_prepared_path: last_run_prepared
val_set_size: 0.05
output_dir: ./outputs/basemodel-swallowmx-8x22b

model_config:
  output_router_logits: true

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true

use_wandb: true
wandb_project: shisa-v2
wandb_entity: augmxnt
wandb_name: shisa-swallowmx-13a47b-v1

global_batch_size: 1
gradient_accumulation_steps: 8
micro_batch_size: 1
num_epochs: 3
# https://github.com/huggingface/transformers/issues/22101
# https://github.com/huggingface/transformers/blob/main/src/transformers/training_args.py#L141
optimizer: paged_adamw_8bit
lr_scheduler: linear
learning_rate: 2e-5

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

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

warmup_ratio: 0.1
evals_per_epoch: 2
eval_table_size:
saves_per_epoch: 1
debug:
deepspeed: axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:

outputs/basemodel-swallowmx-8x22b

This model is a fine-tuned version of tokyotech-llm/Swallow-MX-8x7b-NVE-v0.1 on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4443

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: 2e-05
  • train_batch_size: 1
  • eval_batch_size: 1
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 8
  • gradient_accumulation_steps: 8
  • total_train_batch_size: 64
  • total_eval_batch_size: 8
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 119
  • num_epochs: 3

Training results

Training Loss Epoch Step Validation Loss
0.5705 0.0022 1 0.5065
0.505 0.4993 229 0.3910
0.5258 0.9986 458 0.3654
0.2964 1.4835 687 0.3786
0.2923 1.9828 916 0.3669
0.1462 2.4682 1145 0.4429
0.1156 2.9676 1374 0.4443

Framework versions

  • Transformers 4.40.2
  • Pytorch 2.3.0+cu121
  • Datasets 2.19.1
  • Tokenizers 0.19.1