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
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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