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The was part of some LR ablations. It's not bad but you should probably prefer 8e-6

I ran the tests for 2 runs just to try to lower variance. These are all using temp 0.2, min_p 0.1, freq penalty 0.5

Model AVG Score ELYZA100 JA MT-Bench Rakuda Tengu-Bench JA Char %
shisa-v1-llama3-8b.lr-2e4 3.97 4.60 4.54 3.33 3.42 92.42%
shisa-v1-llama3-8b.lr-5e5 5.73 6.28 6.45 5.37 4.81 90.93%
shisa-v1-llama3-8b (2e5 avg) 6.33 6.51 6.66 6.68 5.48 91.51%
shisa-v1-llama3-8b.8e6 6.59 6.67 6.95 7.05 5.68 91.30%
shisa-v1-llama3-8b.5e6 6.42 6.33 6.76 7.15 5.45 91.56%
shisa-v1-llama3-8b.2e6 6.31 6.26 6.88 6.73 5.38 92.00%
  • The 2e-4 and 5e-5 are definitely overtrained and perform significantly worse.
  • 2e-5 is on the edge since weightwacher shows the embed as slightly overtrained for 2e-5, but NEFTune version is not
  • 8e-6 performs the best, and 5e-6 also performed slightly better than 2e-5

Built with Axolotl

See axolotl config

axolotl version: 0.4.0

base_model: meta-llama/Meta-Llama-3-8B-Instruct
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: false
strict: false

chat_template: llama3
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/lr-5e6

sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true

use_wandb: true
wandb_project: shisa-v2
wandb_entity: augmxnt
wandb_name: shisa-v1-llama3-8b.lr-5e6

gradient_accumulation_steps: 8
micro_batch_size: 1
num_epochs: 3
optimizer: paged_adamw_8bit
lr_scheduler: linear
learning_rate: 5e-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

warmup_steps: 100
evals_per_epoch: 2
eval_table_size:
saves_per_epoch: 0
debug:
deepspeed: axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.00
fsdp:
fsdp_config:
special_tokens:
  pad_token: <|end_of_text|>

outputs/lr-5e6

This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B-Instruct on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5020

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-06
  • 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: 100
  • num_epochs: 3

Training results

Training Loss Epoch Step Validation Loss
1.3951 0.0064 1 0.8645
0.891 0.5020 79 0.5705
0.8575 1.0040 158 0.5243
0.7296 1.4853 237 0.5079
0.7068 1.9873 316 0.4976
0.6618 2.4694 395 0.5020

Framework versions

  • Transformers 4.40.2
  • Pytorch 2.3.0+cu121
  • Datasets 2.19.1
  • Tokenizers 0.19.1
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Safetensors
Model size
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Tensor type
BF16
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Finetuned from

Dataset used to train shisa-ai/shisa-v1-llama3-8b.lr-5e6